JaywalkerVR: 安全上重要な歩行者-車両インタラクションを収集するVRシステム
JaywalkerVR: A VR System for Collecting Safety-Critical Pedestrian-Vehicle Interactions
VRで歩行者が危険な状況に飛び出すロングテールな歩行者-車両インタラクションを安全かつ低コストで収集し、自動運転モデルの精度と堅牢性を向上させるデータセットCARLA-VRを構築した。
著者: Kenta Mukoya, Erica Weng, Rohan Choudhury, Kris Kitani
分類: cs.RO
原文アブストラクト
Developing autonomous vehicles that can safely interact with pedestrians requires large amounts of pedestrian and vehicle data in order to learn accurate pedestrian-vehicle interaction models. However, gathering data that include crucial but rare scenarios - such as pedestrians jaywalking into heavy traffic - can be costly and unsafe to collect. We propose a virtual reality human-in-the-loop simulator, JaywalkerVR, to obtain vehicle-pedestrian interaction data to address these challenges. Our system enables efficient, affordable, and safe collection of long-tail pedestrian-vehicle interaction data. Using our proposed simulator, we create a high-quality dataset with vehicle-pedestrian interaction data from safety critical scenarios called CARLA-VR. The CARLA-VR dataset addresses the lack of long-tail data samples in commonly used real world autonomous driving datasets. We demonstrate that models trained with CARLA-VR improve displacement error and collision rate by 10.7% and 4.9%, respectively, and are more robust in rare vehicle-pedestrian scenarios.
