任务性能：自动驾驶赛车的自动自适应圈速推进
Mission Performance: Automatic and Adaptive Race Pace Progression for Autonomous Racing
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中文总结 AI 辅助
本文提出自动驾驶赛车的任务性能模块，通过按扇区自适应调整目标性能而非改变模型参数，在确保安全的同时加速圈速推进，并在A2RL第二赛季的EAV-25赛车上验证了有效性。
中文摘要 AI 辅助
本文描述了为一辆全自动驾驶赛车实现的任务性能模块，该模块自动管理纵向、横向及综合性能，旨在加速圈速推进同时确保安全。鉴于将抓地力实时估计应用于运动规划器和控制器等关键模块的困难与风险，任务性能模块通过引导这些模块调整其目标性能而非改变车辆模型参数来应对。该模块由预定义的推进序列组成，用于在比赛开始时预热轮胎。随后，系统按扇区持续监控安全与车辆动力学指标，自适应地降低、维持或提高每个扇区的性能水平，逐步收敛至最大允许值。该解决方案的有效性在阿布扎比自动驾驶赛车联赛（A2RL）第二赛季的亚斯码头赛道上，通过全自动Dallara Superformula赛车EAV-25得到了验证。
英文摘要
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.
发表机构
- University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）
机构由 AI 辅助整理，请以论文原文为准。
