Question: An efficient method for processing large-scale graphs on hybrid CPU and GPU systems.

**Similar Documents**: Introducing Pegasus: An In-Memory Social Graph Search System for Massive Scale
Pegasus is an innovative in-memory social graph search system, engineered to efficiently handle trillions of connections among billions of users and entities across a cluster of commodity servers. Inspired by traditional information retrieval techniques, Pegasus incorporates unique features to prioritize results based on social proximity, enhancing the relevance of search outcomes. The system is designed to support complex queries that necessitate multiple iterations to retrieve objects located beyond direct connections, significantly broadening the search scope. Pegasus is capable of processing billions of daily queries with response times under a second, serving as a critical component in the architecture of social platforms' advanced search capabilities. This paper delves into the data model and query language supported by Pegasus, detailing its development and refinement as it evolved to become the cornerstone of enhanced search functionalities on large-scale social networks.

Efficient implementations of the Discrete Fourier Transform (DFT) for GPUs provide good performance with large data sizes, but are not competitive with CPU code for small data sizes. On the other hand, several applications perform multiple DFTs on small data sizes. In fact, even algorithms for large data sizes use a divide-and-conquer approach, where eventually small DFTs need to be performed. We discuss our DFT implementation, which is efficient for multiple small DFTs. One feature of our implementation is the use of the asymptotically slow matrix multiplication approach for small data sizes, which improves performance on the GPU due to its regular memory access and computational patterns. We combine this algorithm with the mixed radix algorithm for 1-D, 2-D, and 3-D complex DFTs. We also demonstrate the effect of different optimization techniques. When GPUs are used to accelerate a component of an application running on the host, it is important that decisions taken to optimize the GPU performance not affect the performance of the rest of the application on the host. One feature of our implementation is that we use a data layout that is not optimal for the GPU so that the overall effect on the application is better. Our implementation performs up to two orders of magnitude faster than cuFFT on an NVIDIA GeForce 9800 GTX GPU and up to one to two orders of magnitude faster than FFTW on a CPU for multiple small DFTs. Furthermore, we show that our implementation can accelerate the performance of a Quantum Monte Carlo application for which cuFFT is not effective. The primary contributions of this work lie in demonstrating the utility of the matrix multiplication approach and also in providing an implementation that is efficient for small DFTs when a GPU is used to accelerate an application running on the host.

Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems.
 EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results.
 EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.

To keep up with the Big Data challenge, parallelized algorithms based on dual decomposition have been proposed to perform inference in Markov random fields. Despite this parallelization, current algorithms struggle when the energy has high order terms and the graph is densely connected. In this paper we propose a partitioning strategy followed by a message passing algorithm which is able to exploit pre-computations. It only updates the high-order factors when passing messages across machines. We demonstrate the effectiveness of our approach on the task of joint layout and semantic segmentation estimation from single images, and show that our approach is orders of magnitude faster than current methods.

We describe matrix computations available in the cluster programming framework, Apache Spark. Out of the box, Spark comes with the mllib.linalg library, which provides abstractions and implementations for distributed matrices. Using these abstractions, we highlight the computations that were more challenging to distribute. When translating single-node algorithms to run on a distributed cluster, we observe that often a simple idea is enough: separating matrix operations from vector operations and shipping the matrix operations to be ran on the cluster, while keeping vector operations local to the driver. In the case of the Singular Value Decomposition, by taking this idea to an extreme, we are able to exploit the computational power of a cluster, while running code written decades ago for a single core. We conclude with a comprehensive set of benchmarks for hardware accelerated matrix computations from the JVM, which is interesting in its own right, as many cluster programming frameworks use the JVM.

S ystems are now being designed that can scale to very large configurations. Examples include parallel architectures with thousands of processors, wireless networks connecting tens of thousands of computers, and parallel database systems capable of processing millions of transactions a second. In some cases, systems have grown considerably larger than envisaged by their designers. Design and development costs for such systems could be significantly reduced if only there were efficient techniques for evaluating design alternatives and predicting their impact on overall system performance metrics. Due to the systems' analytical intractability, simulation is the most common performance evaluation technique for such systems. However, the long execution times needed for sequential simulation models often hamper evaluation. For instance, it can easily take weeks to simulate wireless networks with thousands of mobile nodes that are subject to the interference and signal-fading effects of a realistic environment. Also, the simulation of parallel programs typically suffers from slowdown factors of 30 to 50 per processor. 1 This means that simulation of a parallel program executing for five to 15 minutes on a 128-node multi-processor can take anywhere from a few weeks to a few months—even on state-of-the-art sequential workstations. As the size of the physical system increases, the models' memory requirements can easily exceed the workstations' capacity. The limitations of sequential model execution have led to growing interest in the use of parallel execution for simulating large-scale systems. Widespread use of parallel simulation, however, has been significantly hindered by a lack of tools for integrating parallel model execution into the overall framework of system simulation. Although a number of algorithmic alternatives exist for parallel execution of discrete-event simulation models, performance analysts not expert in parallel simulation have relatively few tools giving them flexibility to experiment with multiple algorithmic or architectural alternatives for model execution. Another drawback to widespread use of simulations is the cost of model design and maintenance. The design and development costs for detailed simulation models for complex systems can easily rival the costs for the physical systems themselves. The simulation environment we developed at UCLA attempts to address some of these issues by providing these features: • An easy path for the migration of simulation models to operational software prototypes. • Implementation on both distributed-and shared-memory platforms and support for a diverse set of parallel simulation protocols. • Support for visual and hierarchical model design. Our environment consists of three primary components: …

Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis'04 contest), the ionization front instability data set (Vis'08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.

Modern graphics processing units (GPUs) employ a large number of hardware threads to hide both function unit and memory access latency. Extreme multithreading requires a complex thread scheduler as well as a large register file, which is expensive to access both in terms of energy and latency. We present two complementary techniques for reducing energy on massively-threaded processors such as GPUs. First, we investigate a two-level thread scheduler that maintains a small set of active threads to hide ALU and local memory access latency and a larger set of pending threads to hide main memory latency. Reducing the number of threads that the scheduler must consider each cycle improves the scheduler’s energy efficiency. Second, we propose replacing the monolithic register file found on modern designs with a hierarchical register file. We explore various trade-offs for the hierarchy including the number of levels in the hierarchy and the number of entries at each level. We consider both a hardware-managed caching scheme and a software-managed scheme, where the compiler is responsible for orchestrating all data movement within the register file hierarchy. Combined with a hierarchical register file, our two-level thread scheduler provides a further reduction in energy by only allocating entries in the upper levels of the register file hierarchy for active threads. Averaging across a variety of real world graphics and compute workloads, the active thread count can be reduced by a factor of 4 with minimal impact on performance and our most efficient three-level software-managed register file hierarchy reduces register file energy by 54%.

Event log processing and analysis play a key role in applications ranging from security management, IT trouble shooting, to user behavior analysis. Recent years have seen a rapid growth in system scales and the corresponding rapid increase in the amount of log event data. At the same time, as logs are found to be a valuable information source, log analysis tasks have become more sophisticated demanding both interactive exploratory query processing and batch computation. Desirable query types include selection with time ranges and value filtering criteria, join within time windows, join between log data and reference tables, and various aggregation types. In such a situation, parallel solutions are necessary, but existing parallel and distributed solutions either support limited query types or perform only batch computations on logs. With a system called LogKV, this paper reports a first study of using Key-Value stores to support log processing and analysis, exploiting the scalability, reliability, and efficiency commonly found in Key-Value store systems. LogKV contains a number of unique techniques that are needed to handle log data in terms of event ingestion, load balancing, storage optimization, and query processing. Preliminary experimental results show that LogKV is a promising solution.

MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.

We present a preprocessing-based acceleration technique for computing bi-criteria Pareto-optimal journeys in public transit networks, based on the well-known RAPTOR algorithm [16]. Our key idea is to first partition a hypergraph into cells, in which vertices correspond to routes (e.g., bus lines) and hyperedges to stops, and to then mark routes sufficient for optimal travel across cells. The query can then be restricted to marked routes and those in the source and target cells. This results in a practical approach, suitable for networks that are too large to be efficiently handled by the basic RAPTOR algorithm. 1998 ACM Subject Classification G.2.2 Graph Theory


Question: An efficient method for processing large-scale graphs on hybrid CPU and GPU systems.

This paper introduces Vulture, a large-scale social search platform designed to efficiently connect users with the most knowledgeable individuals within their extended social networks. When a user poses a question through various channels such as instant messaging, email, web forms, text messages, or voice, Vulture leverages sophisticated routing algorithms to identify and direct the query to the person most qualified to provide an answer. Unlike traditional web search engines, which focus on retrieving the most relevant document, Vulture's primary challenge is to pinpoint the most suitable person to fulfill the user's informational requirements. Moreover, while trust in conventional search engines is rooted in authority, Vulture bases trust on the intimacy of personal connections. The paper elaborates on how these distinctive aspects shape the platform's architecture, the algorithms employed, and the user interface, all of which contribute to the unique behavior patterns observed among Vulture users. Specifically, it discusses how the system integrates both CPU and GPU resources to manage the computational demands of large-scale graph processing, ensuring quick and accurate responses while maintaining the integrity of social connections.

Modern large-scale heterogeneous computers incorporating GPUs offer impressive processing capabilities. It is desirable to fully utilize such systems for serving multiple users concurrently to visualize large data at interactive rates. However, as the disparity between data transfer speed and compute speed continues to increase in heterogeneous systems, data locality becomes crucial for performance. We present a new job scheduling design to support multi-user exploration of large data in a heterogeneous computing environment, achieving near optimal data locality and minimizing I/O overhead. The targeted application is a parallel visualization system which allows multiple users to render large volumetric data sets in both interactive mode and batch mode. We present a cost model to assess the performance of parallel volume rendering and quantify the efficiency of job scheduling. We have tested our job scheduling scheme on two heterogeneous systems with different configurations. The largest test volume data used in our study has over two billion grid points. The timing results demonstrate that our design effectively improves data locality for complex multi-user job scheduling problems, leading to better overall performance of the service.

A lot of real-life data are of graph nature. However, it is not until recently that business begins to exploit data’s connectedness for business insights. On the other hand, RDBMSs are a mature technology for data management, but they are not for graph processing. Take graph traversal, a common graph operation for example, it heavily relies on a graph primitive that accesses a given node’s neighborhood. We need to join tables following foreign keys to access the nodes in the neighborhood if an RDBMS is used to manage graph data. Graph exploration is a fundamental building block of many graph algorithms. But this simple operation is costly due to a large volume of I/O caused by the massive amount of table joins. In this paper, we present G-SQL, our effort toward the integration of a RDBMS and a native in-memory graph processing engine. G-SQL leverages the fast graph exploration capability provided by the graph engine to answer multi-way join queries. Meanwhile, it uses RDBMSs to provide mature data management functionalities, such as reliable data storage and additional data access methods. Specifically, G-SQL is a SQL dialect augmented with graph exploration functionalities and it dispatches query tasks to the in-memory graph engine and its underlying RDMBS. The G-SQL runtime coordinates the two query processors via a unified cost model to ensure the entire query is processed efficiently. Experimental results show that our approach greatly expands capabilities of RDBMs and delivers exceptional performance for SQL-graph hybrid queries.

Now a day’s Peta-Bytes of data becomes the norm in industries. Handling, analyzing such big data is challenging task. Even frameworks like Hadoop (Open Source Implementation of MapReduce Paradigm) and NoSQL databases like Cassandra, HBase can be used to analyze and store such large data; heterogeneity of data is still an issue. Data centers usually have clusters formed using heterogeneous nodes. Ecosystem like Hadoop can be used to manage such types of cluster but it can not schedule jobs (Application) efficiently on this heterogeneous cluster when data is itself heterogeneous. Heterogeneity of data may be because of data format or because of the complexity. This paper is review of systems and algorithms for distributed management of huge size data, efficiency of these approaches.

The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.

Deployers of cloud storage and iterative processing systems typically have to deal with either dollar budget constraints or throughput requirements. This paper examines the question of whether such cloud storage and iterative processing systems are more cost-efficient when scheduled on a COTS (scale out) cluster or a single beefy (scale up) machine. We experimentally evaluate two systems: 1) a distributed key-value store (Cassandra), and 2) a distributed graph processing system (Graph Lab). Our studies reveal scenarios where each option is preferable over the other. We provide recommendations for deployers of such systems to decide between scale up vs. Scale out, as a function of their dollar or throughput constraints. Our results indicate that there is a need or adaptive scheduling in heterogeneous clusters containing scale up and scale out nodes.

Algorithms for Continuous Queries: A Geometric

Physical problems offer scope for macro level parallelization of solution by their essential structure. For parallelization of electrical network simulation, the most natural structure based method is that of multiport decomposition. In this paper this method is used for the simulation of electrical networks consisting of resistances, voltage and current sources using a distributed cluster of weakly coupled processors. At the two levels in which equations are solved in this method the authors have used sparse LU for both levels in the first scheme and sparse LU in the inner level and conjugate gradient in the outer level in the second scheme. Results are presented for planar networks, for the cases where the numbers of slave processors are 1 and 2, and for circuit sizes up to 8.2 million nodes and 16.4 million edges using 8 slave processors. The authors use a cluster of Pentium IV processors linked through a 10/100MBPS Ethernet switch

We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to 83× from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.

With the emergence of accelerator devices such as multicores, graphics-processing units (GPUs), and field-programmable gate arrays (FPGAs), application designers are confronted with the problem of searching a huge design space that has been shown to have widely varying performance and energy metrics for different accelerators, different application domains, and different use cases. To address this problem, numerous studies have evaluated specific applications across different accelerators. In this paper, we analyze an important domain of applications, referred to as sliding-window applications, when executing on FPGAs, GPUs, and multicores. For each device, we present optimization strategies and analyze use cases where each device is most effective. The results show that FPGAs can achieve speedup of up to 11x and 57x compared to GPUs and multicores, respectively, while also using orders of magnitude less energy.

Flint is a simulator that numerically integrates heterogeneous biophysical models described by a large set of ordinary differential equations. It uses an internal bytecode representation of simulation-related expressions to handle various biophysical models built for general purposes. We propose two acceleration methods for Flint using a graphics processing unit (GPU). The first method interprets multiple bytecodes in parallel on the GPU. It automatically parallelizes the simulation using a level scheduling algorithm. We implement an interpreter of the Flint bytecode that is suited for running on the GPU, which reduces both the number of memory accesses and divergent branches to achieve higher performance. The second method translates a model into a source code for both the CPU and the GPU through the internal bytecode, which speeds up the compilation of the generated source codes, because the code size is diminished because of bytecode unification. With large models such that tens of thousands or more expressions can be evaluated simultaneously, the translated code running on the GPU achieves computational performance of up to 2.7 higher than that running on a CPU. Otherwise, with small models, the CPU is faster than the GPU. Therefore, the translated code dynamically determines on which to run either the CPU or the GPU by profiling initial few iterations of the simulation.


Question: An efficient method for processing large-scale graphs on hybrid CPU and GPU systems.

This document introduces an innovative algorithm, Bidirectional Search, for enhancing keyword search efficiency on graph databases. Graph representations of relational, XML, and HTML data, with entities as nodes and relationships as edges, are increasingly common. The challenge lies in effectively retrieving a small set of "optimal" answer trees from these graphs in response to keyword queries. The conventional Backward Expanding search method, which initiates at nodes that match keywords and progresses toward confluent roots, is often employed for text-centric queries. However, this approach can be inefficient if certain keywords correspond to a large number of nodes or if a node has an exceptionally high degree. To address these limitations, the proposed Bidirectional Search algorithm integrates a forward search from potential roots toward leaves, thereby optimizing the search process. A key feature of this algorithm is a prioritization technique for the search frontier, based on spreading activation, which dynamically allocates resources to the most promising search paths. Empirical evaluation on real-world datasets demonstrates the superior performance of Bidirectional Search compared to the Backward Expanding search, highlighting its effectiveness in large-scale graph processing scenarios.

ÐEfficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.

This paper presents VelocityOLAP (VOLAP), a distributed real-time OLAP system for high-velocity data. VOLAP makes use of dimension hierarchies, is highly scalable, exploits both multi-core and multi-processor parallelism, and can guarantee serializable execution of insert and query operations. In contrast to other high performance OLAP systems such as SAP HANA or IBM Netezza that rely on vertical scaling or special purpose hardware, VOLAP supports cost-efficient horizontal scaling on commodity hardware or modest cloud instances. Experiments on 20 Amazon EC2 nodes with TPC-DS data show that VOLAP is capable of bulk ingesting data at over 600 thousand items per second, and processing streams of interspersed insertions and aggregate queries at a rate of approximately 50 thousand insertions and 20 thousand aggregate queries per second with a database of 1 billion items. VOLAP is designed to support applications that perform large aggregate queries, and provides similar high performance for aggregations ranging from a few items to nearly the entire database.

When Google first introduced the Map/Reduce paradigm in 2004, no comparable system had been available to the general public. The situation has changed since then. The Map/Reduce paradigm has become increasingly popular and there is no shortage of Map/Reduce implementations in today's computing world. The predominant solution is currently Apache Hadoop, started by Yahoo. Besides employing custom Map/Reduce installations, customers of cloud services can now exploit ready-made made installations (e.g. the Elastic Map/Reduce System). In the mean time, other, second generation frameworks have started to appear. They either fine tune the Map/Reduce model for specific scenarios, or change the paradigm altogether, such as Google's Pregel. In this paper, we present a comparison between these second generation frameworks and the current de-facto standard Hadoop, by focusing on a specific scenario: large-scale graph analysis. We analyze the different means of fine-tuning those systems by exploiting their unique features. We base our analysis on the k-core decomposition problem, whose goal is to compute the centrality of each node in a given graph; we tested our implementation in a cluster of Amazon EC2 nodes with realistic datasets made publicly available by the SNAP project.

We present SNAP (small-world network analysis and partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering quality in social network analysis. In order to achieve scalable parallel performance, we exploit typical network characteristics of small-world networks, such as the low graph diameter, sparse connectivity, and skewed degree distribution. We conduct an extensive experimental study on real-world graph instances and demonstrate that our parallel schemes, coupled with aggressive algorithm engineering for small-world networks, give significant running time improvements over existing modularity-based clustering heuristics, with little or no loss in clustering quality. For instance, our divisive clustering approach based on approximate edge betweenness centrality is more than two orders of magnitude faster than a competing greedy approach, for a variety of large graph instances on the Sun Fire T2000 multicore system. SNAP also contains parallel implementations of fundamental graph-theoretic kernels and topological analysis metrics (e.g., breadth-first search, connected components, vertex and edge centrality) that are optimized for small- world networks. The SNAP framework is extensible; the graph kernels are modular, portable across shared memory multicore and symmetric multiprocessor systems, and simplify the design of high-level domain-specific applications.

The ubiquity of graphics processing unit (GPU) architectures has made them efficient alternatives to chip-multiprocessors for parallel workloads. GPUs achieve superior performance by making use of massive multi-threading and fast context-switching to hide pipeline stalls and memory access latency. However, recent characterization results have shown that general purpose GPU (GPGPU) applications commonly encounter long stall latencies that cannot be easily hidden with the large number of concurrent threads/warps. This results in varying execution time disparity between different parallel warps, hurting the overall performance of GPUs -- the warp criticality problem.
 To tackle the warp criticality problem, we propose a coordinated solution, criticality-aware warp acceleration (CAWA), that efficiently manages compute and memory resources to accelerate the critical warp execution. Specifically, we design (1) an instruction-based and stall-based criticality predictor to identify the critical warp in a thread-block, (2) a criticality-aware warp scheduler that preferentially allocates more time resources to the critical warp, and (3) a criticality-aware cache reuse predictor that assists critical warp acceleration by retaining latency-critical and useful cache blocks in the L1 data cache. CAWA targets to remove the significant execution time disparity in order to improve resource utilization for GPGPU workloads. Our evaluation results show that, under the proposed coordinated scheduler and cache prioritization management scheme, the performance of the GPGPU workloads can be improved by 23% while other state-of-the-art schedulers, GTO and 2-level schedulers, improve performance by 16% and -2% respectively.

Recently, general-purpose computing on graphics processing units (GPGPU) has been enabled on mobile devices thanks to the emerging heterogeneous programming models such as OpenCL. The capability of GPGPU on mobile devices opens a new era for mobile computing and can enable many computationally demanding computer vision algorithms on mobile devices. As a case study, this paper proposes to accelerate an exemplar-based inpainting algorithm for object removal on a mobile GPU using OpenCL. We discuss the methodology of exploring the parallelism in the algorithm as well as several optimization techniques. Experimental results demonstrate that our optimization strategies for mobile GPUs have significantly reduced the processing time and make computationally intensive computer vision algorithms feasible for a mobile device. To the best of the authors' knowledge, this work is the first published implementation of general-purpose computing using OpenCL on mobile GPUs.

We present two new parallel implementations of the tree-ensemble algorithms Random Forest (RF) and Extremely randomized trees (ERT) for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). Random Forest and Extremely randomized trees are ensemble learners for classification and regression. They operate by constructing a multitude of decision trees at training time and outputting a prediction by comparing the outputs of the individual trees. Thanks to the inherent parallelism of the task, an obvious platform for its computation is to employ contemporary GPUs with a large number of processing cores. Previous parallel algorithms for Random Forests in the literature are either designed for traditional multi-core CPU platforms or early history GPUs with simpler hardware architecture and relatively few number of cores. The new parallel algorithms are designed for contemporary GPUs with a large number of cores and take into account aspects of the newer hardware architectures as memory hierarchy and thread scheduling. They are implemented using the C/C++ language and the CUDA interface for best possible performance on NVidia-based GPUs. An experimental study comparing with the most important previous solutions for CPU and GPU platforms shows significant improvement for the new implementations, often with several magnitudes.

Efficient implementations of the Discrete Fourier Transform (DFT) for GPUs provide good performance with large data sizes, but are not competitive with CPU code for small data sizes. On the other hand, several applications perform multiple DFTs on small data sizes. In fact, even algorithms for large data sizes use a divide-and-conquer approach, where eventually small DFTs need to be performed. We discuss our DFT implementation, which is efficient for multiple small DFTs. One feature of our implementation is the use of the asymptotically slow matrix multiplication approach for small data sizes, which improves performance on the GPU due to its regular memory access and computational patterns. We combine this algorithm with the mixed radix algorithm for 1-D, 2-D, and 3-D complex DFTs. We also demonstrate the effect of different optimization techniques. When GPUs are used to accelerate a component of an application running on the host, it is important that decisions taken to optimize the GPU performance not affect the performance of the rest of the application on the host. One feature of our implementation is that we use a data layout that is not optimal for the GPU so that the overall effect on the application is better. Our implementation performs up to two orders of magnitude faster than cuFFT on an NVIDIA GeForce 9800 GTX GPU and up to one to two orders of magnitude faster than FFTW on a CPU for multiple small DFTs. Furthermore, we show that our implementation can accelerate the performance of a Quantum Monte Carlo application for which cuFFT is not effective. The primary contributions of this work lie in demonstrating the utility of the matrix multiplication approach and also in providing an implementation that is efficient for small DFTs when a GPU is used to accelerate an application running on the host.

A GPU (Graphics Processing Unit) is a specialized processor for graphics processing. GPUs have the ability to perform high-speed parallel processing using its many processing cores. To utilize the powerful computing ability, GPUs are widely used for general purpose processing. The main contribution of this paper is to show a new template matching algorithm using pixel rearrangement. Template Matching is a technique for finding small parts of an image which match a template image. The feature of our proposed algorithm is that using pixel rearrangement, multiple low-resolution images are generated and template matching for the low-resolution images is performed to reduce the computing time. Also, we implemented our algorithm on a GPU system. The experimental results show that, for an input image with size of 4096 $\times$ 4096 and a template image with size of 256 $\times$ 256, our implementation can achieve a speedup factor of approximately 78 times over the conventional sequential implementation.

Design and Optimization of OpenFOAM-based CFD Applications for Modern Hybrid and Heterogeneous HPC Platforms Amani AlOnazi The progress of high performance computing platforms is dramatic, and most of the simulations carried out on these platforms result in improvements on one level, yet expose shortcomings of current CFD packages capabilities. Therefore, hardwareaware design and optimizations are crucial towards exploiting modern computing resources. This thesis proposes optimizations aimed at accelerating numerical simulations, which are illustrated in OpenFOAM solvers. A hybrid MPI and GPGPU parallel conjugate gradient linear solver has been designed and implemented to solve the sparse linear algebraic kernel that derives from two CFD solver: icoFoam, which is an incompressible flow solver, and laplacianFoam, which solves the Poisson equation, for e.g., thermal diffusion. A load-balancing step is applied using heterogeneous decomposition, which decomposes the computations taking into account the performance of each computing device and seeking to minimize communication. In addition, we implemented the recently developed pipeline conjugate gradient as an algorithmic improvement, and parallelized it using MPI, GPGPU, and a hybrid technique. While many questions of ultimately attainable per node performance and multi-node scaling remain, the experimental results show that the hybrid implementation of both solvers significantly outperforms state-of-the-art implementations of a widely used open source package.


Question: Regarding the implementation of ISO 26262 in the design of control systems for autonomous vehicles.

This paper introduces a novel methodology for autonomous driving that incorporates learning from human behavior, specifically focusing on velocity control for automated vehicles. Our approach involves an offline phase where we analyze and model car-following behaviors from a collection of real-world driving scenarios. During online operation, this model is employed to predict and emulate the acceleration decisions a human driver would make in similar circumstances. These predictions serve as a reference for a predictive controller that is tasked with ensuring safety and comfort by applying final acceleration commands while adhering to predefined constraints. The controller is designed to be resilient against uncertainties in the predicted motion of surrounding vehicles. Moreover, we incorporate a mechanism to assess the confidence in the predictions made by the driver model, which is then integrated into the cost function of the predictive controller. This allows our system to effectively manage situations where the training data lacks adequate information on human driving responses. The efficacy of our methodology is substantiated through a series of simulations and practical tests conducted on our autonomous vehicle platform.

The present study investigates automation misuse based on complacency and automation bias in interacting with a decision aid in a process control system. The effect of a preventive training intervention which includes exposing participants to rare automation failures is examined. Complacency is reflected in an inappropriate checking and monitoring of automated functions. In interaction with automated decision aids complacency might result in commission errors, i.e., following automatically generated recommendations even though they are false. Yet, empirical evidence proving this kind of relationship is still lacking. A laboratory experiment (N 1⁄4 24) was conducted using a process control simulation. An automated decision aid provided advice for fault diagnosis and management. Complacency was directly measured by the participants’ information sampling behavior, i.e., the amount of information sampled in order to verify the automated recommendations. Possible commission errors were assessed when the aid provided false recommendations. The results provide clear evidence for complacency, reflected in an insufficient verification of the automation, while commission errors were associated with high levels of complacency. Hence, commission errors seem to be a possible, albeit not an inevitable consequence of complacency. Furthermore, exposing operators to automation failures during training significantly decreased complacency and thus represents a suitable means to reduce this risk, even though it might not avoid it completely. Potential applications of this research include the design of training protocols in order to prevent automation misuse in interaction with automated decision aids. r 2008 Elsevier Ltd. All rights reserved.

The new generation vehicle will provide connectivity and telematics services for enabling vehicle communication. The communication over IP in TCP/IP means enables a connection between external node and invehicle nodes using IP protocols. In order to bring IP into the vehicle communication Ethernet technology is needed. Ethernet technology will provide high speed data transmission. A hardware independent Ethernet driver is necessary for providing Ethernet services. In vehicle diagnostics, the diagnostic tools and vehicles are separated by an internetwork. The main aim of using IP into the family of automotive communication protocol is that the development of new in-vehicle network has led to the need for communication between external equipment and onboard Electronic Control Units (ECUs) using many data link layer technologies. Diagnostic Over IP (DoIP) is a protocol mainly used for communication between off-board and on-board diagnostic system. This will improve the opportunities of interconnecting in-vehicle networks with internet for many new applications, including online, remote automotive diagnostics. Due to the limited resource availability in embedded devices, lightweight TCP/IP implementation is adopted. AutoSAR (Automotive Open System Architecture) is a standardized architecture introduced by a group of automobile manufacturers, suppliers and tool developers for developing and integrating software modules from different vendors.

Backing of tractor-trailer systems is a problem addressed in many literatures. It is usually solved using various nonlinear-based control methods, which are often not easy to implement or tune. Similar to other work focused on backing a single axle trailer with a car like vehicle, we propose a two-tier controller that is simple and intuitive. However, ours is based upon curvature as opposed to hitch angle, which allows the control input to be more directly related to path specification and to handle path curvature discontinuity better. Experimental results are provided to illustrate the capability of this new algorithm applied to a full scale autonomous vehicle and trailer system in a real field environment using minimal sensing capability. Results demonstrate good performance on sloped grounds with various grades.

This paper presents comparison of two control algorithms for DC/DC converters in vehicle chargers. It presents operation in phase-shifting algorithm and resonant LLC algorithm, their implementation, and test results.

In this paper, a methodology is proposed to integrate safety analysis within a systems engineering approach. This methodology is based on SysML models and aims at generating (semi-) automatically safety analysis artifacts, mainly FMEA and FTA, from system models. Preliminary functional and component FMEA are automatically generated from the functional and structural models respectively, then completed by safety experts. By representing SysML structural diagram as a directed multi-graph, through a graph traversal algorithm and some identified patterns, generic fault trees are automatically derived with corresponding logic gates and events. The proposed methodology provides the safety expert with assistance during safety analysis. It helps reducing time and error proneness of the safety analysis process. It also helps ensuring consistency since the safety analysis artifacts are automatically generated from the latest system model version. The methodology is applied to a real case study, the electromechanical actuator EMA.

As more software modules and external interfaces are getting added on vehicles, new attacks and vulnerabilities are emerging. Researchers have demonstrated how to compromise in-vehicle Electronic Control Units (ECUs) and control the vehicle maneuver. To counter these vulnerabilities, various types of defense mechanisms have been proposed, but they have not been able to meet the need of strong protection for safety-critical ECUs against in-vehicle network attacks. To mitigate this deficiency, we propose an anomaly-based intrusion detection system (IDS), called Clock-based IDS (CIDS). It measures and then exploits the intervals of periodic in-vehicle messages for fingerprinting ECUs. The thusderived fingerprints are then used for constructing a baseline of ECUs’ clock behaviors with the Recursive Least Squares (RLS) algorithm. Based on this baseline, CIDS uses Cumulative Sum (CUSUM) to detect any abnormal shifts in the identification errors — a clear sign of intrusion. This allows quick identification of in-vehicle network intrusions with a low false-positive rate of 0.055%. Unlike state-of-the-art IDSs, if an attack is detected, CIDS’s fingerprinting of ECUs also facilitates a rootcause analysis; identifying which ECU mounted the attack. Our experiments on a CAN bus prototype and on real vehicles have shown CIDS to be able to detect a wide range of in-vehicle network attacks.

The recent DARPA-sponsored automated vehicle "Challenges" have generated strong interest in both the research community and the general public, raising consciousness about the possibilities for vehicle automation. Driverless vehicles make good subjects for the visually-oriented media, and they pose enough interesting research challenges to occupy generations of graduate students. However, automated vehicles also have the potential to help solve a variety of real-world problems. Engineers need to think carefully about which of those problems we are actually solving.A well-engineered system should be designed to satisfy specific needs, and those needs should be reflected in the definition of system requirements. Alternative technological approaches can then be evaluated and traded off based on their ability to meet the requirements. The article describes the rather different needs of the public road transportation system and the military transportation system, and then shows how those needs influence the requirements for automated vehicle systems. These requirements point toward significantly different technical approaches, but it is possible to find some limited areas of technical commonality.

We show in this paper that multi-layer dependability is an indispensable way to cope with the increasing amount of technology-induced dependability problems that threaten to proceed further scaling. We introduce the definition of multi-layer dependability and present our design flow within this paradigm that seamlessly integrates techniques starting at circuit layer all the way up to application layer and thereby accounting for ASIC-based architectures as well as for reconfigurable-based architectures. At the end, we give evidence that the paradigm of multi-layer dependability bears a large potential for significantly increasing dependability at reasonable effort.

Road vehicle travel at a reasonable speed involves some risk, even when using computer-controlled driving with failure-free hardware and perfect sensing. A fully-automated vehicle must continuously decide how to allocate this risk without a human driver’s oversight. These are ethical decisions, particularly in instances where an automated vehicle cannot avoid crashing. In this chapter, I introduce the concept of moral behavior for an automated vehicle, argue the need for research in this area through responses to anticipated critiques, and discuss relevant applications from machine ethics and moral modeling research. 1 Ethical Decision Making for Automated Vehicles Vehicle automation has progressed rapidly this millennium, mirroring improvements in machine learning, sensing, and processing. Media coverage often focuses on the anticipated safety benefits from automation, as computers are expected to be more attentive, precise, and predictable than human drivers. Mentioned less often are the novel problems from automated vehicle crash. The first problem is liability, as it is currently unclear who would be at fault if a vehicle crashed while self-driving. The second problem is the ability of an automated vehicle to make ethically-complex decisions when driving, particularly prior to a crash. This chapter focuses on the second problem, and the application of machine ethics to vehicle automation. Driving at any significant speed can never be completely safe. A loaded tractor trailer at 100 km/hr requires eight seconds to come to a complete stop, and a passenger car requires three seconds [1]. Truly safe travel requires accurate predictions of other vehicle behavior over this time frame, something that is simply not possible given the close proximities of road vehicles. To ensure its own safety, an automated vehicle must continually assess risk: the risk of traveling a certain speed on a certain curve, of crossing the centerline to

Context: Self-Driving cars are getting more and more attention with public demonstration from all important automotive OEMs but also from companies, which do not have a long history in the automotive industry. Fostered by large international competitions in the last decade, several automotive OEMs have already announced to bring this technology to the market around 2020. Objective: International competitions like the 2007 DARPA Urban Challenge did not focus on efficient usage of resources to realize the self-driving vehicular functionality. Since the automotive industry is very cost-sensitive, realizing reliable and robust selfdriving functionality is challenging when expensive and sophisticated sensors mounted very visibly on the vehicle’s roof for example cannot be used. Therefore, the goal for this study is to investigate how architectural design decisions of recent self-driving vehicular technology consider resource-efficiency. Method: In a multiple case study, the architectural design decisions derived for resourceconstrained self-driving miniature cars for the international competition CaroloCup are compared with architectural designs from recent real-scale self-driving cars. Results: Scaling down available resources for realizing self-driving vehicular technology puts additional constraints on the architectural design; especially reusability of software components in platform-independent algorithmic concepts are prevailing. Conclusion: Software frameworks like the robotic operating system (ROS) enable fast prototypical solutions; however, architectural support for resource-constrained devices is limited. Here, architectural design drivers as realized in AUTOSAR are more suitable.


Question: Regarding the implementation of ISO 26262 in the control design for autonomous vehicles.

Navigating the Complexities of Autonomous Vehicle Testing and Validation under ISO 26262
In the realm of autonomous vehicle (AV) development, software testing often falls short of a systematic approach, focusing narrowly on bug identification rather than a comprehensive quality assurance strategy. To ensure the safe and scalable deployment of AVs, a more methodical testing process is essential. The ISO 26262 development V model provides a structured framework linking various testing phases to specific design or requirement documents, but it encounters significant hurdles when applied to the unique testing challenges of AVs. This paper highlights five critical areas of testing difficulty for AVs under the V model: the absence of a human driver, intricate requirements, non-deterministic algorithms, learning-based algorithms, and fail-operational systems. To address these challenges, the paper suggests several promising strategies: a phased implementation approach using gradually expanded operational scenarios, an architecture featuring a monitor/actuator pair to separate complex autonomy functions from safety-critical tasks, and the use of fault injection for more efficient edge case testing. While certifying the safety of high-level autonomy algorithms remains a complex issue, the paper argues that it is feasible to design systems and processes that leverage established software safety practices.

The modern automobile is controlled by networked computers. The security of these networks was historically of little concern, but researchers have in recent years demonstrated their many vulnerabilities to attack. As part of a defence against these attacks, we evaluate an anomaly detector for the automotive controller area network (CAN) bus. The majority of attacks are based on inserting extra packets onto the network. But most normal packets arrive at a strict frequency. This motivates an anomaly detector that compares current and historical packet timing. We present an algorithm that measures inter-packet timing over a sliding window. The average times are compared to historical averages to yield an anomaly signal. We evaluate this approach over a range of insertion frequencies and demonstrate the limits of its effectiveness. We also show how a similar measure of the data contents of packets is not effective for identifying anomalies. Finally we show how a one-class support vector machine can use the same information to detect anomalies with high confidence.

Autonomous or semi-autonomous systems are deployed in environments where contact with programmers or technicians is infrequent or undesirable. To operate reliably, such systems should be able to adapt to new circumstances on their own. This paper describes our combined approach for adaptable software architecture and task synthesis from high-level goals, which is based on a three-layer model. In the uppermost layer, reactive plans are generated from goals expressed in a temporal logic. The middle layer is responsible for plan execution and assembling a configuration of domain-specific software components, which reside in the lowest layer. Moreover, the middle layer is responsible for selecting alternative components when the current configuration is no longer viable for the circumstances that have arisen. The implementation demonstrates that the approach enables us to handle non-determinism in the environment and unexpected failures in software components.

The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building of systems that aim at goals or standards which may or may not be specified in explicitly theoretical terms. In this paper we wish to provide some direction for continued research by outlining the value and limitations inherent in each of these approaches.

This article presents a distributed dissimilar redundancy architecture of fly-by-wire Flight Control System. It is based on many dissimilar flight control computer, redundancy structure which includes the cross arranged hydraulic and power supply, three types of data buses, lots of data correction monitors, a distributed control loop to avoid the correlated faults and achieve the rigid safety airworthiness requirements. A Fault Tree Analysis (FTA) is implemented to verify whether the system safety could meet system design targets.

This study explores, in the context of semiautonomous driving, how the content of the verbalized message accompanying the car’s autonomous action affects the driver’s attitude and safety performance. Using a driving simulator with an auto-braking function, we tested different messages that provided advance explanation of the car’s imminent autonomous action. Messages providing only “how” information describing actions (e.g., “The car is braking”) led to poor driving performance, whereas “why” information describing reasoning for actions (e.g., “Obstacle ahead”) was preferred by drivers and led to better driving performance. Providing both “how and why” resulted in the safest driving performance but increased negative feelings in drivers. These results suggest that, to increase overall safety, car makers need to attend not only to the design of autonomous actions but also to the right way to explain these actions to the drivers.

Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency.

The regulatory, ethical, and legal barriers imposed on medical robots necessitate careful consideration of different levels of autonomy, as well as the context for use.

Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers for self-driving cars, this raises concerns about their safety. We develop a novel automated verification framework for feed-forward multi-layer neural networks based on Satisfiability Modulo Theory (SMT). We focus on image manipulations, such as scratches or changes to camera angle or lighting conditions, and define safety for an image classification decision in terms of invariance of the classification with respect to manipulations of the original image within a region of images that are close to it. We enable exhaustive search of the region by employing discretisation, and propagate the analysis layer by layer. Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations. If found, adversarial examples can be shown to human testers and/or used to fine-tune the network. We implement the techniques using Z3 and evaluate them on state-of-the-art networks, including regularised and deep learning networks. We also compare against existing techniques to search for adversarial examples and estimate network robustness.

To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

This paper describes a hybrid model and a model predictive control (MPC) strategy for solving a traction control problem. The problem is tackled in a systematic way from modeling to control synthesis and implementation. The model is described first in the Hybrid Systems Description Language to obtain a mixed-logical dynamical (MLD) hybrid model of the open-loop system. For the resulting MLD model, we design a receding horizon finite-time optimal controller. The resulting optimal controller is converted to its equivalent piecewise affine form by employing multiparametric programming techniques, and finally experimentally tested on a car prototype. Experiments show that good and robust performance is achieved in a limited development time by avoiding the design of ad hoc supervisory and logical constructs usually required by controllers developed according to standard techniques.


Question: Regarding the implementation of ISO 26262 in the design of control systems for autonomous vehicles.

This research introduces a novel methodology for enabling autonomous vehicles to execute lane changes in a personalized and secure manner, without requiring explicit driver input (such as activating turn signals). Traditionally, lane change initiation in self-driving cars is determined by rigid rules that consider the positions and velocities of nearby vehicles. However, these rules lack flexibility and do not accommodate individual driving styles. To address this, we propose a machine learning technique to model and emulate human driver behavior during lane changes. Our approach involves collecting real-world data from a test vehicle in various lane change scenarios and training classifiers to anticipate the precise moment a driver would initiate a lane change, tailored to their specific preferences. This predictive decision-making logic is then integrated into a model predictive control (MPC) framework, resulting in a personalized autonomous lane change system that adheres to safety and comfort parameters. Our simulations showcase the capability of the decision logic to replicate and distinguish between distinct driving styles, while also demonstrating the safety and efficacy of the control framework in managing lane changes. This work represents a significant step toward enhancing the adaptability and personalization of autonomous driving systems, making them more attuned to individual driver preferences.

Recognition of necessities of connected and automated vehicles (CAVs) is gaining momentum. CAVs can improve both transportation network efficiency and safety through control algorithms that can harmonically use all existing information to coordinate the vehicles. This paper addresses the problem of optimally coordinating CAVs at merging roadways to achieve smooth traffic flow without stop-and-go driving. We present an optimization framework and an analytical closed-form solution that allows online coordination of vehicles at merging zones. The effectiveness of the efficiency of the proposed solution is validated through a simulation, and it is shown that coordination of vehicles can significantly reduce both fuel consumption and travel time.

Mobile road works on the hard shoulder of German highways bear an increased accident risk for the crew of the protective vehicle which safeguards road works against moving traffic. The project “Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works” aims at the unmanned operation of the protective vehicle in order to reduce this risk. Simultaneously, this means the very first unmanned operation of a vehicle on German roads in public traffic. This contribution introduces the project by pointing out the main objectives and demonstrates the current state of the system design regarding functionality, modes of operation, as well as the functional system architecture. Pivotal for the project, the scientific challenges raised by the unmanned operation - strongly related to the general challenges in the field of automated driving - are presented as well. The results of the project shall serve as a basis to stimulate an advanced discussion about ensuring safety for fully automated vehicles in public traffic operating at higher speeds and in less defined environments. Thus, this contribution aims at contacting the scientific community in an early state of the project.

Automotive manufacturers have been taking advantage of simulation tools for modeling and analyzing various types of vehicles, such as conventional, electric, and hybrid electric vehicles. These simulation tools are of great assistance to engineers and researchers to reduce product-development cycle time, improve the quality of the design, and simplify the analysis without costly and time-consuming experiments. In this paper, a modeling tool that has been developed to study automotive systems using the power electronics simulator (PSIM) software is presented. PSIM was originally made for simulating power electronic converters and motor drives. This user-friendly simulation package is able to simulate electric/electronic circuits; however, it has no capability for simulating the entire system of an automobile. This paper discusses the PSIM validity as an automotive simulation tool by creating module boxes for not only the electrical systems, but also the mechanical, energy-storage, and thermal systems of the vehicles. These modules include internal combustion engines, fuel converters, transmissions, torque couplers, and batteries. Once these modules are made and stored in the library, the user can make the car model either a conventional, an electric, or a hybrid vehicle at will, just by dragging and dropping onto a schematic blank page.

The automotive industry is moving aggressively in the direction of advanced active safety. Dedicated short-range communication (DSRC) is a key enabling technology for the next generation of communication-based safety applications. One aspect of vehicular safety communication is the routine broadcast of messages among all equipped vehicles. Therefore, channel congestion control and broadcast performance improvement are of particular concern and need to be addressed in the overall protocol design. Furthermore, the explicit multichannel nature of DSRC necessitates a concurrent multichannel operational scheme for safety and non-safety applications. This article provides an overview of DSRC based vehicular safety communications and proposes a coherent set of protocols to address these requirements

The paper addresses the problem of autonomous navigation of a car-like robot evolving in an urban environment. Such an environment exhibits an heterogeneous geometry and is cluttered with moving obstacles. Furthermore, in this context, motion safety is a critical issue. The proposed approach to the problem lies in the coupling of two crucial robotic capabilities, namely perception and planning. The main contributions of this work are the development and integration of these modules into one single application, considering explicitly the constraints related to the environment and the system

Against the backdrop of increasing progresses in AI research paired with a rise of AI applications in decision-making processes, security-critical domains as well as in ethically relevant frames, a largescale debate on possible safety measures encompassing corresponding long-term and short-term issues has emerged across different disciplines. One pertinent topic in this context which has been addressed by various AI Safety researchers is e.g. the AI alignment problem for which no final consensus has been achieved yet. In this paper, we present a multidisciplinary toolkit of AI Safety strategies combining considerations from AI and Systems Engineering as well as from Cognitive Science with a security mindset as often relevant in Cybersecurity. We elaborate on how AGI “Self-awareness” could complement different AI Safety measures in a framework extended by a jointly performed Human Enhancement procedure. Our analysis suggests that this hybrid framework could contribute to undertake the AI alignment problem from a new holistic perspective through security-building synergetic effects emerging thereof and could help to increase the odds of a possible safe future transition towards superintelligent systems.

In vehicular ad hoc network (VANET), vehicles equipped with computing, sensing, and communication capabilities can exchange information within a geographical area to distribute emergency messages and achieve safety system. Then how to enforce fine grained control of these messages and ensure the receiving messages coming from the claimed source in such a highly dynamic environments remains a key challenge that affects the quality of service. In this paper, we propose a hierarchical access control with authentication scheme for transmitted messages with security assurance over VANET. By extending ciphertext-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multiple authorities, the scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control on the transmitted messages. Also by exploiting attribute-based signature (ABS), the scheme can authorize the vehicles that can most appropriately deal with the message efficiently. The results of efficiency analysis and comparison with the related works show that the proposed scheme is efficient and scalable in dealing with access control and message authentication for data dissemination in VANET. & 2014 Elsevier B.V. All rights reserved.

The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.

The new generation vehicle will provide connectivity and telematics services for enabling vehicle communication. The communication over IP in TCP/IP means enables a connection between external node and invehicle nodes using IP protocols. In order to bring IP into the vehicle communication Ethernet technology is needed. Ethernet technology will provide high speed data transmission. A hardware independent Ethernet driver is necessary for providing Ethernet services. In vehicle diagnostics, the diagnostic tools and vehicles are separated by an internetwork. The main aim of using IP into the family of automotive communication protocol is that the development of new in-vehicle network has led to the need for communication between external equipment and onboard Electronic Control Units (ECUs) using many data link layer technologies. Diagnostic Over IP (DoIP) is a protocol mainly used for communication between off-board and on-board diagnostic system. This will improve the opportunities of interconnecting in-vehicle networks with internet for many new applications, including online, remote automotive diagnostics. Due to the limited resource availability in embedded devices, lightweight TCP/IP implementation is adopted. AutoSAR (Automotive Open System Architecture) is a standardized architecture introduced by a group of automobile manufacturers, suppliers and tool developers for developing and integrating software modules from different vendors.

Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.


Question: Regarding the implementation of ISO 26262 in the design of control systems for autonomous vehicles.

This paper delves into the utilization of kinematic and dynamic vehicle models for the purpose of model-based control design in the realm of autonomous driving. Specifically, we investigate the statistical characteristics of the prediction error associated with both models, employing empirical data for analysis. Furthermore, we explore how discretization impacts prediction error. Drawing on the findings from our initial analysis, we propose a control strategy for autonomous vehicles, employing model predictive control (MPC) in conjunction with a straightforward kinematic bicycle model. Our suggested methodology offers a computational advantage over conventional techniques that rely on vehicle tire models, and it remains viable at low speeds where tire models may encounter singularity issues. Empirical outcomes validate the efficacy of our proposed strategy across a range of speeds on roads with significant wind conditions. Additionally, we discuss the relevance of these findings in the context of adhering to safety standards outlined in ISO 26262, highlighting the potential for enhanced control design in automated vehicles.

The Carolo-Cup competition conducted for the eighth time this year, is an international student competition focusing on autonomous driving scenarios implemented on 1:10 scale car models. Three practical sub-competitions have to be realized in this context and represent a complex, interdisciplinary challenge. Hence, students have to cope with all core topics like mechanical development, electronic design, and programming as addressed usually by robotic applications. In this paper we introduce the competition challenges in detail and evaluate the results of all 13 participating teams from the 2014 competition. For this purpose, we analyze technical as well as non-technical configurations of each student group and derive best practices, lessons learned, and criteria as a precondition for a successful participation. Due to the comprehensive orientation of the Carolo-Cup, this knowledge can be applied on comparable projects and related competitions as well.

In vehicular ad hoc network (VANET), vehicles equipped with computing, sensing, and communication capabilities can exchange information within a geographical area to distribute emergency messages and achieve safety system. Then how to enforce fine grained control of these messages and ensure the receiving messages coming from the claimed source in such a highly dynamic environments remains a key challenge that affects the quality of service. In this paper, we propose a hierarchical access control with authentication scheme for transmitted messages with security assurance over VANET. By extending ciphertext-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multiple authorities, the scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control on the transmitted messages. Also by exploiting attribute-based signature (ABS), the scheme can authorize the vehicles that can most appropriately deal with the message efficiently. The results of efficiency analysis and comparison with the related works show that the proposed scheme is efficient and scalable in dealing with access control and message authentication for data dissemination in VANET. & 2014 Elsevier B.V. All rights reserved.

Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.

Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure, and to the Internet, thereby becoming an integral part of the Internet of Things. New comfort features, safety functions, and a number of new vehicle-specific services will be integrated in future smart vehicles. These include a fast, secure, and reliable way to diagnose and reconfigure a vehicle, as well as the installation of new software (SW) on its integrated electronic control units (ECUs). Such wireless SW updates are beneficial for both automotive carmakers and customers, as they allow us to securely enable new features on the vehicle and to fix SW bugs by installing a new SW version over the air. A secure and dependable wireless SW update process is valuable in the entire lifetime of a modern vehicle as it can be used already during vehicle development and manufacturing process on the assembly line, as well as during vehicle maintenance in a service center. Additionally, future vehicles will allow us to remotely download up-to-date SW on the ECUs. To support this process over the entire vehicle's lifetime, a generic framework is needed. In this paper, SecUp, a generic framework enabling secure and efficient wireless automotive SW updates is proposed. SecUp utilizes IEEE 802.11s as wireless medium to interconnect vehicles and diagnostic devices in a dependable and fast way. Additionally, SecUp is enabling beneficial wireless SW update features such as parallel and partial SW updates to increase the efficiency, and comprises advanced security mechanisms to prevent abuse and attacks.

The modern automobile is controlled by networked computers. The security of these networks was historically of little concern, but researchers have in recent years demonstrated their many vulnerabilities to attack. As part of a defence against these attacks, we evaluate an anomaly detector for the automotive controller area network (CAN) bus. The majority of attacks are based on inserting extra packets onto the network. But most normal packets arrive at a strict frequency. This motivates an anomaly detector that compares current and historical packet timing. We present an algorithm that measures inter-packet timing over a sliding window. The average times are compared to historical averages to yield an anomaly signal. We evaluate this approach over a range of insertion frequencies and demonstrate the limits of its effectiveness. We also show how a similar measure of the data contents of packets is not effective for identifying anomalies. Finally we show how a one-class support vector machine can use the same information to detect anomalies with high confidence.

As modern systems become increasingly complex, current security practices lack effective methodologies to adequately address the system security. This paper proposes a repeatable and tailorable framework to assist in the application of systems security engineering (SSE) processes, activities, and tasks as defined in the recently released National Institute of Standards and Technology (NIST) Special Publication 800–160. First, a brief survey of systems-oriented security methodologies is provided. Next, an examination of the relationships between the NIST-defined SSE processes is conducted to provide context for the engineering problem space. These findings inform a mapping of the NIST SSE processes to seven system-agnostic security domains which enable prioritization for three types of systems (conventional IT, cyber-physical, and defense). These concrete examples provide further understanding for applying and prioritizing the SSE effort. The goal of this paper is assist practitioners by informing the efficient application of the 30 processes, 111 activities, and 428 tasks defined in NIST SP 800–160. The customizable framework tool is available online for developers to employ, modify, and tailor to meet their needs.

The recent DARPA-sponsored automated vehicle "Challenges" have generated strong interest in both the research community and the general public, raising consciousness about the possibilities for vehicle automation. Driverless vehicles make good subjects for the visually-oriented media, and they pose enough interesting research challenges to occupy generations of graduate students. However, automated vehicles also have the potential to help solve a variety of real-world problems. Engineers need to think carefully about which of those problems we are actually solving.A well-engineered system should be designed to satisfy specific needs, and those needs should be reflected in the definition of system requirements. Alternative technological approaches can then be evaluated and traded off based on their ability to meet the requirements. The article describes the rather different needs of the public road transportation system and the military transportation system, and then shows how those needs influence the requirements for automated vehicle systems. These requirements point toward significantly different technical approaches, but it is possible to find some limited areas of technical commonality.

This study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between q = 0.80 and q = 0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders. 2015 Elsevier Ltd. All rights reserved.

The vision of autonomous driving is piecewise becoming reality. Still the problem of executing the driving task in a safe and comfortable way in all possible environments, for instance highway, city or rural road scenarios is a challenging task. In this paper we present a novel approach to planning trajectories for autonomous vehicles. Hereby we focus on the problem of planning a trajectory, given a specific behavior option, e.g. merging into a specific gap at a highway entrance or a roundabout. Therefore we explicitly take arbitrary road geometry and prediction information of other traffic participants into account. We extend former contributions in this field by providing a flexible problem description and a trajectory planner without specialization to distinct classes of maneuvers beforehand. Using a carefully chosen representation of the dynamic free space, the method is capable of considering multiple lanes including the predicted dynamics of other traffic participants, while being real-time capable at the same time. The combination of those properties in one general planning method represents the novelty of the proposed method. We demonstrate the capability of our algorithm to plan safe trajectories in simulation and in real traffic in real-time.

Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.