Architecture And System Support For Safety Aware Autonomous Vehicle Design
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Architecture and System Support for Safety-aware Autonomous Vehicle Design
Recently, autonomous vehicle development ignited competition among car makers and technical corporations. Low-level automation cars are already commercially available. However, the high automated vehicle where the vehicle drives by itself without human monitoring is still at infancy. Such autonomous vehicles (AVs) rely on the AV system to ensure safety. The AV system consists of two key components: data centers and onboard systems. Data centers are responsible for training deep neural network models, which will be used in the onboard system. It is necessary for data centers to train models efficiently within limited periods. The AV onboard system act as human drivers, as it monitors surroundings and plans a route for the AV to drive. To ensure safety, the AV onboard system needs to make timely and appropriate driving decisions. Moreover, a safety validation stage for the onboard system is also required to guarantee AVs' safe operations. To address the above mentioned challenges, this dissertation proposes three designs. In Chapter 2, this dissertation proposes a processing-in-memory architecture to accelerate deep neural network training stage, which reduces the data movement overhead and improves energy efficiency. In Chapter 3, this dissertation presents the safety score, a latency based safety metric, and the latency model, that represents the correlation between perception latency and surrounding obstacle distribution; then it presents the resource management scheme to optimize safety and performance. In Chapter 4, this dissertation presents Suraksha, a generalized safety validation framework that includes a set of safety metrics and driving scenarios; it also employs Suraksha to perform a case study, where the perception module is studied and safety effects can be collected and analyzed by changing selected perception parameters.
Autonomous Vehicles for Safer Driving
Self-driving cars are no longer in the realm of science fiction, thanks to the integration of numerous automotive technologies that have matured over many years. Technologies such as adaptive cruise control, forward collision warning, lane departure warning, and V2V/V2I communications are being merged into one complex system. The papers in this compendium were carefully selected to bring the reader up to date on successful demonstrations of autonomous vehicles, ongoing projects, and what the future may hold for this technology. It is divided into three sections: overview, major design and test collaborations, and a sampling of autonomous vehicle research projects. The comprehensive overview paper covers the current state of autonomous vehicle research and development as well as obstacles to overcome and a possible roadmap for major new technology developments and collaborative relationships. The section on major design and test collaborations covers Sartre, DARPA contests, and the USDOT and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications (CAMP-VSC2) Consortium. The final section presents seven SAE papers on significant recent and ongoing research by individual companies on a variety of approaches to autonomous vehicles. This book will be of interest to a wide range of readers: engineers at automakers and electronic component suppliers; software engineers; computer systems analysts and architects; academics and researchers within the electronics, computing, and automotive industries; legislators, managers, and other decision-makers in the government highway sector; traffic safety professionals; and insurance and legal practitioners.
Advanced Driver Assistance Systems and Autonomous Vehicles
This book provides a comprehensive reference for both academia and industry on the fundamentals, technology details, and applications of Advanced Driver-Assistance Systems (ADAS) and autonomous driving, an emerging and rapidly growing area. The book written by experts covers the most recent research results and industry progress in the following areas: ADAS system design and test methodologies, advanced materials, modern automotive technologies, artificial intelligence, reliability concerns, and failure analysis in ADAS. Numerous images, tables, and didactic schematics are included throughout. This essential book equips readers with an in-depth understanding of all aspects of ADAS, providing insights into key areas for future research and development. • Provides comprehensive coverage of the state-of-the-art in ADAS • Covers advanced materials, deep learning, quality and reliability concerns, and fault isolation and failure analysis • Discusses ADAS system design and test methodologies, novel automotive technologies • Features contributions from both academic and industry authors, for a complete view of this important technology