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

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arxiv 2202.07008 v1 pith:M6TDBYJW submitted 2022-02-14 cs.RO cs.SE

classification cs.ROcs.SE
keywords autonomousfieldracinghighresearchersresearchsurveyvehicles
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Self driving algorithm for an active four wheel drive racecar

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A PPO agent learns end-to-end control of steering and four independent wheel torques in TORCS, implicitly discovering torque-vectoring and traction-stability behaviors.

  2. R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing

    cs.RO 2025-06 reject novelty 5.0 of 10

    R-CARLA integrates custom vehicle dynamics, opponents, and digital-twin maps into CARLA, reporting reduced sim-to-real gaps for racing stacks, but its sensor-simulation improvement is not measured against real sensor data.

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