複雑な都市環境における歩行者を考慮した自動運転の運動計画
Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios
歩行者の社会的力を用いたリスク考慮型運動計画アルゴリズムを提案し、2Dシミュレーションで混雑環境での安全で効率的な自動運転を実現した。
著者: Korbinian Moller, Truls Nyberg, Jana Tumova, Johannes Betz
分類: cs.RO
原文アブストラクト
Motion planning in uncertain environments like complex urban areas is a key challenge for autonomous vehicles (AVs). The aim of our research is to investigate how AVs can navigate crowded, unpredictable scenarios with multiple pedestrians while maintaining a safe and efficient vehicle behavior. So far, most research has concentrated on static or deterministic traffic participant behavior. This paper introduces a novel algorithm for motion planning in crowded spaces by combining social force principles for simulating realistic pedestrian behavior with a risk-aware motion planner. We evaluate this new algorithm in a 2D simulation environment to rigorously assess AV-pedestrian interactions, demonstrating that our algorithm enables safe, efficient, and adaptive motion planning, particularly in highly crowded urban environments - a first in achieving this level of performance. This study has not taken into consideration real-time constraints and has been shown only in simulation so far. Further studies are needed to investigate the novel algorithm in a complete software stack for AVs on real cars to investigate the entire perception, planning and control pipeline in crowded scenarios. We release the code developed in this research as an open-source resource for further studies and development. It can be accessed at the following link: https://github.com/TUM-AVS/PedestrianAwareMotionPlanning
