Lab2Car: 実験的プランナーを複雑な実環境に展開するための汎用ラッパー
Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-world Environments
任意の運動プランナーが出す軌道のスケッチを、安全で快適かつ動的に実行可能な軌道に変換する最適化ベースのラッパーを提案し、ラスベガスの自動運転車でMLプランナーと古典的プランナーを実環境展開して有効性を示した。
著者: Marc Heim, Francisco Suarez-Ruiz, Ishraq Bhuiyan, Bruno Brito, Momchil S. Tomov
分類: cs.RO
原文アブストラクト
Human-level autonomous driving is an ever-elusive goal, with planning and decision making -- the cognitive functions that determine driving behavior -- posing the greatest challenge. Despite a proliferation of promising approaches, progress is stifled by the difficulty of deploying experimental planners in naturalistic settings. In this work, we propose Lab2Car, an optimization-based wrapper that can take a trajectory sketch from an arbitrary motion planner and convert it to a safe, comfortable, dynamically feasible trajectory that the car can follow. This allows motion planners that do not provide such guarantees to be safely tested and optimized in real-world environments. We demonstrate the versatility of Lab2Car by using it to deploy a machine learning (ML) planner and a classical planner on self-driving cars in Las Vegas. The resulting systems handle challenging scenarios, such as cut-ins, overtaking, and yielding, in complex urban environments like casino pick-up/drop-off areas. Our work paves the way for quickly deploying and evaluating candidate motion planners in realistic settings, ensuring rapid iteration and accelerating progress towards human-level autonomy.
