Social Force Model-Based Adaptive Parameters Collision Avoidance Method Considering Motion Uncertainty of the Pedestrian

In typical traffic scenarios such as non-signalized roads or shared spaces where vehicles and pedestrians interact without clear separations. The interaction distance between objects is usually shorter due to the simultaneous motion of road users. Pedestrian-crossing scenarios in these areas make the scenario complex due to the unpredictability of the pedestrians intention and the need to balance between safety, comfort, and time consumption. To address this collision avoidance(CA) problem, a novel strategy using Social Force Model (SFM)-based adaptive parameters was proposed. The interaction system between the ego vehicle and the pedestrian was simplified as a Markov process to adopt the SFM-based dynamic model, and the validity of this simplification was demonstrated using real-world driving data. Based on the current state of the interaction system that consists of vehicle and pedestrian, this research adopted the optimal parameters that were generated by particle swarm optimization (PSO) to generate optimal parameters for the SFM-based vehicle dynamic model, which helps the vehicle avoid pedestrians with random motion. The proposed method was validated through bench testing, and the results showed that the proposed method balanced the safety, comfort, and time consumption requirements during the CA process in the studied scenario.