Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning

• URL: http://arxiv.org/abs/2209.15133v1
• Date: Thu, 29 Sep 2022 23:23:38 GMT
• Title: Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning
• Authors: Hongyu Guo, Kun Xie and Mehdi Keyvan-Ekbatani
• Abstract summary: This study aims to develop a lane-change-related evasive behavior model. It can facilitate the development of safety-aware traffic simulations and predictive collision avoidance systems.
• Score: 19.649145869208617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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