Pedestrian trajectory prediction method based on group perception

Most methods do not model the pedestrian groups in autonomous driving, which will have an impact on road traffic safety. Therefore, a group perception pedestrian trajectory prediction network called GPCNet was proposed. Specifically, in intra-group, the interaction between pedestrian was learned at the individual level and the preference issue of different pedestrian was considered. In inter-group, the interaction between pedestrian groups was learned at the group level and the collision issue of pedestrian trajectory was considered using the social force model. Simulation results demonstrate that GPCNet improves the performance on the ETH and UCY datasets by 75.4% compared to the commonly used trajectory prediction methods.