Computing Systems for Autonomous Driving

This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving.

Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies.This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving.

Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies.

Introduces promising new research topics in autonomous driving computing systems Deals with the many remaining challenges to achieving full and safe autonomy In addition to computing systems design, examines Vehicle-to-everything (V2X) technologies and hardware platforms

Autorentext

Weisong Shi is a Professor of Computer Science with Wayne State University, where he leads the Wayne Mobility Initiative (WMI) and directs the Connected and Autonomous Driving Laboratory (CAR) investigating performance, reliability, power- and energy-efficiency, trust and privacy issues of computing systems for CAVs. He is the Founding Steering Committee Chair of International Conference on Connected and Autonomous Driving (MetroCAD) and the chair of the IEEE Special Technical Community on Autonomous Driving Technologies. He is an IEEE Fellow and an ACM Distinguished Scientist.

Liangkai Liu is a PhD candidate at Wayne State University. His research is on connected and autonomous vehicles and edge computing. He has published papers in top conference and journals including SEC, ICDCS, HotEdge, Proceedings of IEEE, IoTJ, etc. He is the reviewer for several journals in transportation and computer systems domain including IEEE Communications Magazine, TVT, IoTJ, IEEE Network Magazine, etc. He is the instructor for Autonomous Vehicle Technologies online Course at Wayne State University.

Inhalt
Autonomous Driving Landscape.- Computing Framework for Autonomous Driving.- Algorithm Deployment Optimization.- Systems Runtime Optimization.- Dataset and Benchmark.- Autonomous Driving Simulators.- Hardware Platforms.- Smart Infrastructure for Autonomous Driving.- Challenges and Open Problems.