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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.22937 |
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| _version_ | 1866918152135245824 |
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| author | Weiss, Trent Kulkarni, Amar Behl, Madhur |
| author_facet | Weiss, Trent Kulkarni, Amar Behl, Madhur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22937 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DBF-MA: A Differential Bayesian Filtering Planner for Multi-Agent Autonomous Racing Overtakes Weiss, Trent Kulkarni, Amar Behl, Madhur Robotics 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. |
| title | DBF-MA: A Differential Bayesian Filtering Planner for Multi-Agent Autonomous Racing Overtakes |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.22937 |