Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing

Abstract:Autonomous racing confronts significant challenges in safely overtaking Opponent Vehicles (OVs) that exhibit uncertain trajectories, stemming from unknown driving policies. To address these challenges, this study proposes heterogeneous kernel metrics for Deep Kernel Learning (DKL), designed to robustly capture the diverse driving policies of OVs, and carry out precise trajectory predictions along with the associated uncertainties. A key virtue of the proposed kernel metrics lies in their ability to align similar driving policies and disjoin dissimilar ones in an unsupervised manner, given the observed interactions between the Ego Vehicle (EV) and OVs. The efficacy of the proposed method is substantiated through experimental studies on a 1/10th scale racecar platform, demonstrating improved prediction accuracy and thereby safely overtaking against OVs. Furthermore, our method is computationally efficient for onboard computing units, affirming its viability in fast-paced racing environments. The video and source code can be found at this https URL.

Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.17147 [cs.RO]

(or arXiv:2609.17147v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.17147 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

IEEE Robotics and Automation Letters, vol. 9, no. 12, pp. 11050-11057, Dec. 2024

https://doi.org/10.1109/LRA.2024.3486178 Focus to learn more DOI(s) linking to related resources

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