Study on Collision Avoidance Behavior in the Social Force-Based Pedestrian–Vehicle Interaction Simulation Model at Unsignalized Intersections - Xuwei Wang; Tingting Liu; Zhen Liu (Applied Sciences)
Modeling pedestrian–vehicle interaction behaviors not only helps better predict the intentions and actions of traffic participants but also contributes to generating more realistic pedestrian trajectories for testing autonomous vehicles. Most existing pedestrian–vehicle interaction models use repulsive forces toward target directions to avoid collisions. However, pedestrian agents in these models lack the ability to plan avoidance routes based on their positions when facing conflicting vehicles, leading to poor simulation effects at unsignalized intersections. By analyzing the crossing trajectories of pedestrians at unsignalized intersections through video data, we observed that when participants reject a current vehicle gap, they may tend to move toward the vehicle’s rear to start crossing the traffic flow earlier, thereby obtaining a safer opportunity to cross the road. In contrast, most previous pedestrian–vehicle interaction models only simulated pedestrians’ avoidance by moving away from vehicles. In response, we propose a pedestrian–vehicle interaction model incorporating pedestrian avoidance tendencies, which is based on the social force framework. Our improvements include refining the vehicle’s influence on pedestrians in lateral and longitudinal dimensions. The pedestrian agents in this model can make appropriate crossing decisions and select collision avoidance paths according to traffic conditions. This model can simulate pedestrian–vehicle interaction scenarios at unsignalized intersections and can be extended to pedestrian safety testing for autonomous vehicles.