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

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., dedicated short-range communication, cellular vehicle-to-everything, 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 article, we present state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by 12 challenges to realize autonomous driving. We hope this article will gain attention from both the computing and automotive communities and inspire more research in this direction.

Automated summary

The key to the success of current autonomous driving systems lies in making reliable real-time decisions, while the real traffic environment is too complex for these systems to fully grasp. This article introduces state-of-the-art computing systems for autonomous driving and highlights the challenges to be addressed, aiming to draw attention from the industry and inspire further research.