教訓学習によるパーソナライズ自動車線変更の進化加速
Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning
ドライバーの介入データからオンラインで学習し、パーソナライズされた自動車線変更を効率的に進化させる手法を提案。
著者: Jia Hu, Mingyue Lei, Haoran Wang, Zeyu Liu, Fan Yang
分類: cs.LG, cs.RO
原文アブストラクト
Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot computing power and cannot be applied online. To address this challenge, this paper proposes a lesson learning approach: learning from driver's takeover interventions. By leveraging online takeover data, the driving zone is generated to ensure perceived safety using Gaussian discriminant analysis. Real-time corrections to trajectory planning rewards are enacted through apprenticeship learning. Guided by the objective of optimizing rewards within the constraints of the driving zone, this approach employs model predictive control for trajectory planning. This lesson learning framework is highlighted for its faster evolution capability, adeptness at experience accumulating, assurance of perceived safety, and computational efficiency. Simulation results demonstrate that the proposed system consistently achieves a successful customization without further takeover interventions. Accumulated experience yields a 24% enhancement in evolution efficiency. The average number of learning iterations is only 13.8. The average computation time is 0.08 seconds.
