aiqbench analysis
This research presents a novel control module for autonomous racing that dynamically adjusts vehicle performance targets based on real-time safety metrics rather than altering underlying physical models. By managing tire warm-up and sector-specific dynamics, the system accelerates lap time progression while maintaining operational safety in fully autonomous environments. The approach addresses critical challenges in motion planning and controller adaptation for high-speed robotic systems.
Mission Performance: Automatic and Adaptive Race Pace Progression for Autonomous Racing
arXiv:2609.12292v1 Announce Type: new Abstract: In this paper, we describe the Mission Performance module implemented for a fully autonomous racing car to automatically manage the longitudinal, lateral, and combined performances, aiming to speedup the laptime progression while assuring safety. Motivated by the difficulty and risks of applying the real-time estimation of the grip to critical modules like the motion planner and controller, the Mission Performance guides these modules adapting their target performance instead of changing the vehicle model parameters. The module is formed by pre-defined progressions to warm up the tires at the beginning of a run. Then, the system continuously monitors safety and vehicle dynamics metrics on a per-sector basis to adaptively reduce, maintain, or increase the performance levels for each sector, progressively converging toward the maximum allowed value. The solution's effectiveness is demonstrated on the EAV-25, a fully autonomous Dallara Superformula, at the Yas Marina Circuit during the Abu Dhabi Autonomous Racing League (A2RL) Season 2.
Abstract:In this paper, we describe the Mission Performance module implemented for a fully autonomous racing car to automatically manage the longitudinal, lateral, and combined performances, aiming to speedup the laptime progression while assuring safety. Motivated by the difficulty and risks of applying the real-time estimation of the grip to critical modules like the motion planner and controller, the Mission Performance guides these modules adapting their target performance instead of changing the vehicle model parameters. The module is formed by pre-defined progressions to warm up the tires at the beginning of a run. Then, the system continuously monitors safety and vehicle dynamics metrics on a per-sector basis to adaptively reduce, maintain, or increase the performance levels for each sector, progressively converging toward the maximum allowed value. The solution's effectiveness is demonstrated on the EAV-25, a fully autonomous Dallara Superformula, at the Yas Marina Circuit during the Abu Dhabi Autonomous Racing League (A2RL) Season 2.
Robotics (cs.RO); Software Engineering (cs.SE); Systems and Control (eess.SY)
arXiv:2609.12292 [cs.RO]
(or arXiv:2609.12292v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.12292 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 253 words
