基于核度量的不确定对手车辆轨迹预测方法在自动驾驶赛车中的应用
Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing
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中文总结 AI 辅助
本研究提出基于深度核学习的异构核度量方法，用于预测自动驾驶赛车中不确定轨迹的对手车辆，通过无监督对齐相似驾驶策略提升预测精度，并在1/10比例平台上验证了安全超越的有效性和计算效率。
中文摘要 AI 辅助
自动驾驶赛车在安全超越具有不确定轨迹的对手车辆（OVs）时面临重大挑战，这些不确定性源于对手车辆未知的驾驶策略。为解决这些问题，本研究提出了用于深度核学习（DKL）的异构核度量，旨在稳健地捕捉对手车辆多样的驾驶策略，并实现精确的轨迹预测及其相关不确定性估计。所提核度量的关键优势在于，在观察到自我车辆（EV）与对手车辆之间交互的基础上，能够以无监督方式对齐相似的驾驶策略并分离不相似的策略。通过在1/10比例赛车平台上的实验研究，验证了所提方法的有效性，展示了预测精度的提升，从而实现了对对手车辆的安全超越。此外，我们的方法在计算上对车载计算单元高效，证实了其在快节奏赛车环境中的可行性。视频和源代码可在以下网址找到：此https URL。
英文摘要
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.
发表机构
- Ulsan National Institute of Science and Technology（蔚山国立科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
