Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing

TL;DR

This survey comprehensively reviews autonomous racing, covering algorithms, platforms, and challenges, highlighting recent advancements and future research directions in high-performance autonomous racecars.

Contribution

First holistic survey of autonomous racing covering perception, planning, control, end-to-end learning, and hardware-software co-evolution with insights from leading researchers.

Findings

Overview of current autonomous racing algorithms and methods

Analysis of autonomous racing platforms and hardware-software integration

Identification of open research challenges and future directions

Abstract

The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods and approaches that are used in the fields of perception, planning and control as well as end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to a range of high performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms emphasizing both the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field we conclude with a summary of open research challenges that will guide future researchers in this field.