基于人-车交互的行人轨迹预测

针对行人轨迹预测具有复杂、拥挤的场景和社会交互问题,基于长短时记忆网络(Long Short-term Memory Network,LSTM)对行人与车辆、行人与其他行人的交互进行建模,提出一种基于人-车交互的行人轨迹预测模型(VP-LSTM).该模型同时考虑了行人与行人的交互、行人与车辆的交互,更适用于复杂的交通场...

Pedestrian Trajectory Prediction Based on Human-vehicle Interaction

A pedestrian trajectory prediction model (VP-LSTM) based on a long short-term memory (LSTM) network was proposed in this study to model the interaction between pedestrians and vehicles that can fit to complex and crowded scenes and social-interaction problems of pedestrian trajectory prediction. The model considers the interaction between pedestrians and vehicles, which is suitable for complex traffic scenarios. The VP-LSTM included three inputs: the direction and speed of pedestrians as historical track sequence inputs, the relative position of pedestrians as human-human interaction information input, and the relative position of pedestrians and vehicles as the human-vehicle interaction information input. First, the fan-shaped human-human and circular human-vehicle interaction neighborhoods were designed to accurately capture the pedestrians and vehicles that interacted with the predicted pedestrians. Second, three LSTM coding layers were established to encode the historical pedestrian-track sequence and the human-human and human-vehicle social information. Third, the anticollision function and direction attention function of human-vehicle and human-human interaction were defined as the weights of human-vehicle and human-human social information, respectively, to improve the accuracy of social information. Then, the information of human-human and human-vehicle interactions was inputted into the attention module to obtain the information focused on by that pedestrians. Finally, the filtered social information and pedestrian history track sequence were inputted into the LSTM neural network to predict the pedestrian trajectory. Finally, the DUT human-vehicle interaction dataset constructed by our group was used to verify the proposed network. The experimental results show that the proposed method can accurately predict the future movement-trajectory of pedestrians in traffic and improve the accuracy of intelligent vehicle decisions.