Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing
arXiv:2202.07008 · doi:10.1109/ojits.2022.3181510
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.
29 pages, 12 figures, 6 tables, 242 references
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Cited by in corpus (9)
- Autonomous Drone Racing: A Survey
- TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge
- Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds
- Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing
- MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing
- Competitive Driving of Autonomous Vehicles
- Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack
- Hierarchical Control for Head-to-Head Autonomous Racing
- Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing