A Sequence-to-Sequence Car-Following Model for Addressing Driver Reaction Delay and Cumulative Error in Multi-Step Prediction

Car-following behavior is one of the most common driving behaviors. To reduce the impact of driver reaction delay and accumulated errors in predicting long sequences on the accuracy of speed prediction, we propose a deep learning car-following model based on a sequence-to-sequence (seq2seq) architecture with an attention mechanism. Firstly, we analyze the characteristics of driver reaction delay during the car-following process and design an attention mechanism to learn the probability distribution of driver reaction delay. This allows the model to consider more environmental information at the moment when the driver actually makes a decision, rather than just the current environmental information, during the prediction process. By utilizing the seq2seq architecture to model car-following behavior, the model focuses more on reducing the impact of accumulated errors during the training process. Additionally, we propose a temporal consistency constraint loss to improve the robustness and stability of the car-following model’s training method and enhance the prediction results. Finally, we use Gated Recurrent Units (GRU) and the sequence-to-sequence model (seq2seq) as baselines, and simulation results demonstrate that our model achieves more accurate car-following behavior prediction.