Computing Systems for Autonomous Driving: State-of-the-Art and Challenges
It addresses the problem of unreliable real-time decision-making in autonomous vehicles for the computing and automotive communities, but it is incremental as it synthesizes existing knowledge rather than presenting new solutions.
This paper reviews state-of-the-art computing systems for autonomous driving, outlining performance metrics, key technologies, and challenges, aiming to inspire further research in the field.
The recent proliferation of computing technologies (e.g., sensors, computer vision, machine learning, and 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 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 current autonomous driving computing systems to understand and handle. In this paper, we present state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by twelve challenges 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.