DBF-MA: A Differential Bayesian Filtering Planner for Multi-Agent Autonomous Racing Overtakes

2025-09-26

AI Summary

Autonomous racing vehicles struggle to perform safe and efficient overtaking maneuvers on complex tracks; existing approaches often rely on simplifying assumptions—such as spherical vehicle approximations, linearized constraints, or conservative collision bounds—that compromise planning robustness. Method: This paper proposes a novel trajectory planning framework based on differential Bayesian filtering. It formulates overtaking trajectory generation as a Bayesian inference problem within the composite Bézier curve space, directly incorporating kinematic constraints and multi-agent collision-avoidance priors—without geometric or dynamic simplifications. Derivative-free optimization enables computationally efficient inference. Contribution/Results: In closed-loop evaluation, the method achieves an 87% overtaking success rate, substantially outperforming state-of-the-art optimization- and graph-search-based planners. It establishes a verifiable paradigm for real-time, multi-agent trajectory planning in high-dynamic, low-margin scenarios.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Generating overtaking maneuvers for autonomous racing agents
• Overcoming oversimplified collision-avoidance and dynamic constraints
• Achieving collision-free trajectory synthesis without derivative requirements

Innovation

• Differential Bayesian Filtering for trajectory synthesis
• Bayesian Inference over Composite Bezier Curves
• Derivative-free collision avoidance without approximations