Computing Systems for Autonomous Driving: State-of-the-Art and Challenges

30 Sep 2020  ·  Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu, Yongtao Yao, Qingyang Zhang, Weisong Shi ·

The recent proliferation of computing technologies, e.g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e.g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors. The key to the success of these autonomous systems is making a reliable decision in a real-time fashion. However, accidents and fatalities caused by early deployed autonomous vehicles arise from time to time. The real traffic environment is too complicated for the current autonomous driving computing systems to understand and handle. In this paper, we present the state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by eleven challenges and opportunities to realize autonomous driving. We hope this paper will gain attention from both the computing and automotive communities and inspire more research in this direction.

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Distributed, Parallel, and Cluster Computing Robotics