面向竞争者的电动耐力赛车管理
Competitor-aware Race Management for Electric Endurance Racing
AI总结：
本文提出一种面向竞争者的双层框架，结合博弈最优控制与强化学习，用于解决电动耐力赛车中的多智能体博弈问题，通过模拟比赛验证了气动交互对比赛结果的关键影响。
摘要：
电动耐力赛车以严重的能量约束和强烈的气动相互作用为特征。确定获胜策略成为根本性的多智能体、博弈论问题。这些策略必须共同管理低层驾驶员输入和高层战略决策，包括能量管理和充电。本文提出了一种面向竞争者的双层框架，结合博弈最优控制与强化学习。在底层，解决多智能体博弈最优控制问题以捕捉气动效应和非对称避碰约束。使用此单圈问题作为环境，强化学习代理被训练以在整场比赛中分配电池能量并安排维修站。该框架在两个智能体、45圈模拟比赛中得到验证。结果表明，有效利用气动相互作用对比赛结果至关重要，优先考虑名次的策略与单智能体最小时间方法有根本区别。
英文摘要：
Electric endurance racing is characterized by severe energy constraints and strong aerodynamic interactions. Determining race-winning policies therefore becomes a fundamentally multi-agent, game-theoretic problem. These policies must jointly govern low-level driver inputs as well as high-level strategic decisions, including energy management and charging. This paper proposes a bi-level framework for competitor-aware race management that combines game-theoretic optimal control with reinforcement learning. At the lower level, a multi-agent game-theoretic optimal control problem is solved to capture aerodynamic effects and asymmetric collision-avoidance constraints inspired by motorsport rules. Using this single-lap problem as the environment, reinforcement learning agents are trained to allocate battery energy and schedule pit stops over an entire race. The framework is demonstrated in a two-agent, 45-lap simulated race. The results show that effective exploitation of aerodynamic interactions is decisive for race outcome, with strategies that prioritize finishing position differing fundamentally from single-agent, minimum-time approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
