Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning
This work addresses safety-critical lane changes for traffic simulation and collision avoidance systems, representing an incremental improvement with a novel method for a known bottleneck.
The study tackled the problem of modeling driver evasive behavior during safety-critical lane changes by proposing a new surrogate safety measure, 2D-TTC, which showed high correlation with actual crashes, and using deep reinforcement learning to replicate behaviors, demonstrating superiority in replicating both longitudinal and lateral evasive actions.
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.