基于贝叶斯滤波的多智能体超车规划，无需简化假设即可安全超车。
DBF-MA: A Differential Bayesian Filtering Planner for Multi-Agent Autonomous Racing Overtakes
- 用贝叶斯推理在复合贝塞尔曲线空间中生成轨迹，免去线性化与近似
- 闭环测试中87%场景成功超车，优于现有方法
- 适合高动态复杂赛道的无人赛车超车任务
自主赛车中生成超车动作是一项重大挑战。赛车智能体需在复杂赛道上执行动作，容错空间极小。已有优化技术和图基方法常依赖对避障和动态约束的过度简化假设。本文提出一种基于微分贝叶斯滤波框架扩展的轨迹合成方法。该方法将无碰撞轨迹生成建模为复合贝塞尔曲线空间上的贝叶斯推断问题。所提方法无需导数、不依赖车辆轮廓的球形近似、无需线性化约束或避障的简化上界。通过闭环分析发现，DBF-MA在测试场景中87%成功实现超车，性能超越现有自主超车方法。
原文摘要 · Abstract (English)
A significant challenge in autonomous racing is to generate overtaking maneuvers. Racing agents must execute these maneuvers on complex racetracks with little room for error. Optimization techniques and graph-based methods have been proposed, but these methods often rely on oversimplified assumptions for collision-avoidance and dynamic constraints. In this work, we present an approach to trajectory synthesis based on an extension of the Differential Bayesian Filtering framework. Our approach for collision-free trajectory synthesis frames the problem as one of Bayesian Inference over the space of Composite Bezier Curves. Our method is derivative-free, does not require a spherical approximation of the vehicle footprint, linearization of constraints, or simplifying upper bounds on collision avoidance. We conduct a closed-loop analysis of DBF-MA and find it successfully overtakes an opponent in 87% of tested scenarios, outperforming existing methods in autonomous overtaking.
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