论文标题
在安全至关重要的车道变化过程中对驾驶员的逃避行为进行建模：二维时间碰撞和深入的增强学习
Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning
论文作者
论文摘要
车道变化是复杂的驾驶行为，经常涉及至关重要的情况。这项研究旨在开发与车道变化相关的回避行为模型，这可以促进安全感知的交通模拟和预测性碰撞避免系统的发展。该研究使用了安全试验模型部署（SPMD）计划的大规模连接的车辆数据。提出了一种新的替代安全措施，即二维时间碰撞（2D-TTC），以确定车道变化期间的安全关键情况。通过显示检测到的冲突风险与存档崩溃之间的高度相关性，确认了2D-TTC的有效性。深层确定性的策略梯度（DDPG）算法可以在连续的动作空间上学习连续的决策过程，用于在确定的安全 - 关键的情况下对回避行为进行建模。结果表明，提出的模型在复制纵向和横向逃避行为方面的优越性。
Lane changes are complex driving behaviors and frequently involve safety-critical situations. This study aims to develop a lane-change-related evasive behavior model, which can facilitate the development of safety-aware traffic simulations and predictive collision avoidance systems. Large-scale connected vehicle data from the Safety Pilot Model Deployment (SPMD) program were used for this study. A new surrogate safety measure, two-dimensional time-to-collision (2D-TTC), was proposed to identify the safety-critical situations during lane changes. The validity of 2D-TTC was confirmed by showing a high correlation between the detected conflict risks and the archived crashes. A deep deterministic policy gradient (DDPG) algorithm, which could learn the sequential decision-making process over continuous action spaces, was used to model the evasive behaviors in the identified safety-critical situations. The results showed the superiority of the proposed model in replicating both the longitudinal and lateral evasive behaviors.
