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

TL;DR

This paper reviews the current state-of-the-art computing systems for autonomous driving, highlighting key technologies, performance metrics, and challenges to improve safety and reliability in complex traffic environments.

Contribution

It provides a comprehensive overview of existing computing architectures, metrics, and challenges, aiming to guide future research in autonomous driving systems.

Findings

Identifies seven performance metrics for autonomous driving computing systems.

Summarizes nine key technologies used in autonomous vehicle computing.

Outlines twelve challenges to achieving reliable autonomous driving.

Abstract

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.