Abstract:

A blind spot passing strategy method was proposed by using the deep reinforcement learning for intelligent connected vehicles (ICV) to prevent traffic accidents between vehicles and pedestrians when passing through visual blind spots. A mathematical description model was established for typical blind spot scenarios considering three indicators of safety, efficiency and comfort; while a deep reinforcement learning model was designed based on the Double DQN (double deep Q-network) with the TTC (time to collision) indicator to establish a set of physically interpretable reward functions, with the output being the vehicle's accelerator and the brake pedal depth. Simulation experiments were conducted under three scenarios to assess the algorithm efficacy. The results show that the simulation experiments verify the effectiveness of the algorithm. The comfort is increased by more than 50% on average of this method, compared with the traditional DQN method. The method improves decision-making accuracy. Therefore, the longitudinal decision-making method achieves the safety, the efficient and the comfortable.

Key words: intelligent connected vehicle (ICV), deep reinforcement learning, pedestrian collision avoidance, time to collision (TTC)