HyPlan: 不確実性下での安全な自動運転のためのハイブリッド学習支援計画
HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving
部分観測交通環境での衝突回避ナビゲーションのため、マルチエージェント行動予測、PPOによる深層強化学習、オンラインPOMDP計画を組み合わせたハイブリッド計画手法を提案し、安全性と実行速度を両立させた。
著者: Donald Pfaffmann, Matthias Klusch, Marcel Steinmetz
分類: cs.RO, cs.AI
原文アブストラクト
We present a novel hybrid learning-assisted planning method, named HyPlan, for solving the collision-free navigation problem for self-driving cars in partially observable traffic environments. HyPlan combines methods for multi-agent behavior prediction, deep reinforcement learning with proximal policy optimization and approximated online POMDP planning with heuristic confidence-based vertical pruning to reduce its execution time without compromising safety of driving. Our experimental performance analysis on the CARLA-CTS2 benchmark of critical traffic scenarios with pedestrians revealed that HyPlan may navigate safer than selected relevant baselines and perform significantly faster than considered alternative online POMDP planners.