Predicting Lane Change and Vehicle Trajectory With Driving Micro-Data and Deep Learning

In the evolving landscape of mixed human-machine driving environments, autonomous vehicles (AVs) confront the challenge of anticipating the lane-changing intentions and subsequent driving trajectories of neighboring vehicles. This capability is essential for optimizing safety, efficiency, and comfort in decision-making processes. This paper introduces a novel hybrid prediction model, the LSTM-GAT-Bilayer-GRU, which leverages deep learning to enhance predictive accuracy and real-time responsiveness in dynamic traffic scenarios. The proposed model consists of two main components: a lane change prediction model (LSTM-GAT) and a trajectory prediction model (G-BiLayer-GRU), to process and predict complex vehicular interactions and environmental dynamics effectively. The efficacy of this integrated model was tested using the HighD dataset for training, validation, and testing purposes. The results of a benchmark analysis indicate that the proposed model demonstrated superior prediction performance and reliability over the Support Vector Machine (SVM), Random Forest (RF), AlexNet and Back-Propagation Through Time (BPTT) in the context of lane change intention recognition. Combining LSTM for temporal data processing with GAT for spatial interaction analysis, along with the GRU's precise trajectory prediction, achieved the best error evaluation metric and balanced prediction time consuming metric under the six prediction time-interval, marks a substantial advancement in AVs technology. This integration guarantees smooth operation of AVs in intricate driving scenarios, fine-tuning their reactions to bolster road safety and passenger comfort.