Multi-head attention-based intelligent vehicle lane change decision and trajectory prediction model in highways

作者：Junyu Cai, Haobin Jiang, Junyan Wang, Aoxue Li · 发表于：Journal of Intelligent Transportation Systems · 年份：2024 · DOI：10.1080/15472450.2024.2341392 · 被引用次数：16 · 研究领域：Autonomous Vehicle Technology and Safety、Traffic Prediction and Management Techniques、Anomaly Detection Techniques and Applications

With the aim to improve the interaction between intelligent vehicles and human drivers, this article proposes the MCLG (multi-head attention + convolutional social pooling + long short-term memory + Gaussian mixture model) lane change decision and trajectory prediction model, which includes a lane-changing intention decision module. The model comprises a lane change decision module responsible for determining three lane change intentions: left lane change, right lane change, and car-following. Subsequently, a multi-head attention mechanism processes complex vehicle interaction information to enhance modeling accuracy and intelligence. In addition, uncertainty in trajectory prediction is considered by using multimodal trajectory prediction and Gaussian mixture model, and diversity and uncertainty are combined by combining trajectory prediction from several different modalities through probabilistic combinatorial sampling patterns. Test results indicate that the MCLG model, based on the multi-head attention module, outperforms existing methods in trajectory prediction. The decision module, which takes interactive information into account, exhibits higher predictability and accuracy. Furthermore, the MCLG model, considering the lane-changing decision module, significantly enhances trajectory prediction accuracy, providing robust decision-making support for autonomous driving systems.