Vehicle Trajectory Prediction With Interaction Regions and Spatial–Temporal Attention

Abstract

Vehicle trajectory prediction is a key technology in autonomous driving, one of the aims of which is to ensure the safety of vehicle travelling and lane changing. Especially in complex traffic scenarios, it is critical to accurately predict the vehicle trajectory so that the automated driving system can make appropriate response actions to help the vehicle driver avoid accidents to the greatest extent possible. Some studies on vehicle trajectory prediction have used recurrent networks (LSTMs) to extract temporal correlations and additional convolutional neural networks (CNNs) to capture spatial correlations. This hybrid model can be added more operations, so we propose a new CNN-LSTM hybrid model containing multi-module, interaction regions and spatial-temporal attention mechanisms (IA-CSTM). In this paper, the traffic scene is divided into multiple interaction regions, and vehicles in different regions have different impacts on the predicted vehicles. The model has multiple modules that deal with vehicle information in different interaction regions. The model will cross-fuse vehicle information in important interaction regions to enhance the model’s perception of vehicle information. Multiple modules are linked through a spatial-temporal attention mechanism and capture the relevance of the vehicle in the interaction region to predicted vehicle.