トラック区間ベースのオフライン方策学習による自動運転レースカーの追い越しマニューバ
Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
トラックを区間に分割し、自車の速度と位置に基づく追い越し確率をオフライン学習で推定し、学習方策を組み込んだ切替型MPCC制御で追い越し回数を増やせることを示した論文。
著者: Jayanth Bhargav, Johannes Betz, Hongrui Zheng, Rahul Mangharam
分類: cs.RO, cs.GT
原文アブストラクト
The rising popularity of driver-less cars has led to the research and development in the field of autonomous racing, and overtaking in autonomous racing is a challenging task. Vehicles have to detect and operate at the limits of dynamic handling and decisions in the car have to be made at high speeds and high acceleration. One of the most crucial parts in autonomous racing is path planning and decision making for an overtaking maneuver with a dynamic opponent vehicle. In this paper we present the evaluation of a track based offline policy learning approach for autonomous racing. We define specific track portions and conduct offline experiments to evaluate the probability of an overtaking maneuver based on speed and position of the ego vehicle. Based on these experiments we can define overtaking probability distributions for each of the track portions. Further, we propose a switching MPCC controller setup for incorporating the learnt policies to achieve a higher rate of overtaking maneuvers. By exhaustive simulations, we show that our proposed algorithm is able to increase the number of overtakes at different track portions.
