自律レースにおける不確実な対戦車両軌道予測のためのカーネルベース指標学習
Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing
自律レースで未知の運転方針を持つ対戦車両の多様な挙動を、深層カーネル学習による異種カーネル指標で教師なしに捉え、不確実性付きで高精度に軌道予測する手法を提案し、1/10スケール車両で安全な追い越しを実証した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Hojin Lee, Youngim Nam, Sanghun Lee, Cheolhyeon Kwon
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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 https://github.com/HMCL-UNIST/OpponentPredictionWithKMDKL.git.