TY - JOUR
T1 - Autonomous Vehicles on the Edge
T2 - A Survey on Autonomous Vehicle Racing
AU - Betz, Johannes
AU - Zheng, Hongrui
AU - Liniger, Alexander
AU - Rosolia, Ugo
AU - Karle, Phillip
AU - Behl, Madhur
AU - Krovi, Venkat
AU - Mangharam, Rahul
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2022
Y1 - 2022
N2 - The rising popularity of self-driving cars has led to the emergence of a new research field in 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 vehicle's 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 used in the areas of perception, planning, control, and end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to high-performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms, emphasizing 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.
AB - The rising popularity of self-driving cars has led to the emergence of a new research field in 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 vehicle's 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 used in the areas of perception, planning, control, and end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to high-performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms, emphasizing 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.
KW - Autonomous systems
KW - advanced driver assistance
KW - autonomous vehicles
KW - control
KW - intelligent vehicles
KW - path planning
KW - simultaneous localization and mapping
UR - http://www.scopus.com/inward/record.url?scp=85143840660&partnerID=8YFLogxK
U2 - 10.1109/OJITS.2022.3181510
DO - 10.1109/OJITS.2022.3181510
M3 - Article
AN - SCOPUS:85143840660
SN - 2687-7813
VL - 3
SP - 458
EP - 488
JO - IEEE Open Journal of Intelligent Transportation Systems
JF - IEEE Open Journal of Intelligent Transportation Systems
ER -