Study on Collision Avoidance Strategies Based on Social Force Model Considering Stochastic Motion of Pedestrians in Mixed Traffic Scenario

In typical traffic scenarios where there are no clear separations between the traffic participants, such as mixed traffic or shared space, vehicles and pedestri-ans are usually moving in the same time so that ego vehicle may need to face with multiple pedestrians in a relatively short interaction distance. Consider-ing the stochastic motion of pedestrians and to bal-ance the time consumption and safety during passing process, this paper proposes two strategies of colli-sion avoidance (CA) for ego vehicle, which are based on model predictive control (MPC) and social force model (SFM). Besides, a modified SFM-based pedes-trian model that considers the stochastic motion is given to evaluate the effectiveness of the proposed strategies. For MPC-based CA strategy, considering the unpredictable motion of the pedestrians, a novel speed re-planning layer combined with collision prob-ability estimation, which is used to calculate an ac-ceptable maximum safe speed for ego vehicle, is pro-posed. On the other hand, parameters associated with the SFM-based vehicle model are re-calibrated by par-ticle swarm optimization (PSO) and the calibration process has been analyzed physically in details. The recommended values based on different initial interac-tion speed and distance of vehicle and pedestrians are also determined for further reference as useful find-ings from the analysis.