Driver Perspective Trajectory Prediction Based on Spatiotemporal Fusion LSTM Network

One of the most important tasks in the autonomous driving environment perception system is to predict the trajectories of surrounding traffic objects, which the output trajectories can provide information for vehicle control, decision-making, and path planning. Considering traditional trajectory prediction usually based on bird-view trajectory prediction, which couldn't satisfy real demand of onboard autonomous driving environment perception. In this paper, we proposed a novel driver perspective trajectory prediction algorithm, which combines of LSTM (Long Short-Term Memory) module, spatial interaction module, and temporal behavior attention module. In order to reflect the interaction and uncertainty between traffic objects and surrounding environment, we modeled the traffic objects as traffic agents. As LSTM network module, the SR-LSTM method was employed to combine single agent's information with surrounding neighbor agents' hidden information to mine historical trajectory information. As spatial interaction module, graph modeling was performed on the agents, and the spatial interactions between the current agent and surrounding agents was analyzed, which using the graph space interaction method. For temporal behavior attention, the driving behaviors of agents was classified into fine-grained categories, the influence of temporal attention on the agent's driving behavior was estimated. Finally, the above spatial module and temporal module were used to enhance the original trajectory results to output final refinement trajectories of traffic agents under driver perspective. To verify the effectiveness of proposed method, we operated our algorithm on the D2-City driving recorder benchmark that can reflect China's complex traffic characteristics, and performed quantitative and qualitative comparative analysis with competitive trajectory prediction algorithms: vanilla LSTM, Social LSTM, and Social GAN. The research results show that the algorithm proposed in this paper can achieve competitive results under different input and prediction durations. In comparison with other three methods, the proposed algorithm's final displacement error index is reduced more than 20% on average. The proposed method can significantly improve the trajectory prediction accuracy, which is suitable for automatic driving environment perception. © 2022, Editorial Department of China Journal of Highway and Transport. All right reserved.