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

By Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu, Yongtao Yao, Qingyan Zhang, Weisong Shi. IEEE Internet of Things Journal, 2020

396 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cf547a66cdd51a5b095864ba715bcaaf2fad5dec

Citations in the corpus