Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms

Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system applications ranging from traffic simulations to driver assistance. The need for this ability is pronounced with the emergence of autonomous vehicles as they require the prediction of nearby vehicles’ trajectories to navigate safely and efficiently. Recent studies based on deep learning have greatly improved prediction accuracy. However, one prominent issue of these models is the lack of model explainability. We alleviate this issue by proposing spatiotemporal attention long short-term memory (STA-LSTM), an LSTM model with spatial-temporal attention mechanisms for explainability in vehicle trajectory prediction. STA-LSTM not only achieves comparable prediction performance against other state-of-the-art models but, more importantly, explains the influence of historical trajectories and neighboring vehicles on the target vehicle. We provide in-depth analyses of the learned spatial–temporal attention weights in various highway scenarios based on different vehicle and environment factors, including target vehicle class, target vehicle location, and traffic density. A demonstration illustrating that STA-LSTM can capture and explain fine-grained lane-changing behaviors is also provided. The data and implementation of STA-LSTM can be found at https://github.com/leilin-research/VTP.