EDRF: マルチモーダル軌道予測に基づく拡張運転リスク場とその応用
EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applications
深層学習によるマルチモーダル軌道予測とガウス分布モデルを統合し、交通参加者の不確実性を定量化する拡張運転リスク場（EDRF）を提案。交通リスク監視、自車リスク分析、運動・軌道計画への応用を示す。
著者: Junkai Jiang, Zeyu Han, Yuning Wang, Mengchi Cai, Qingwen Meng, Qing Xu, Jianqiang Wang
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Driving risk assessment is crucial for both autonomous vehicles and human-driven vehicles. The driving risk can be quantified as the product of the probability that an event (such as collision) will occur and the consequence of that event. However, the probability of events occurring is often difficult to predict due to the uncertainty of drivers' or vehicles' behavior. Traditional methods generally employ kinematic-based approaches to predict the future trajectories of entities, which often yield unrealistic prediction results. In this paper, the Enhanced Driving Risk Field (EDRF) model is proposed, integrating deep learning-based multimodal trajectory prediction results with Gaussian distribution models to quantitatively capture the uncertainty of traffic entities' behavior. The applications of the EDRF are also proposed. It is applied across various tasks (traffic risk monitoring, ego-vehicle risk analysis, and motion and trajectory planning) through the defined concept Interaction Risk (IR). Adequate example scenarios are provided for each application to illustrate the effectiveness of the model.
