Lane-changing trajectory prediction based on multi-task learning

As a complex driving behaviour, lane-changing (LC) behaviour has a great influence on traffic flow. Improper lane-changing behaviour often leads to traffic accidents. Numerous studies are currently being conducted to predict lane-change trajectories to minimize dangers. However, most of their models focus on how to optimize input variables without considering the interaction between output variables. This study proposes an LC trajectory prediction model based on a multi-task deep learning framework to improve driving safety. Concretely, in this work, the coupling effect of lateral and longitudinal movement is considered in the LC process. Trajectory changes in two directions will be modelled separately, and the information interaction is completed under the multi-task learning framework. In addition, the trajectory fragments are clustered by the driving features, and trajectory type recognition is added to the trajectory prediction framework as an auxiliary task. Finally, the prediction process of lateral and longitudinal trajectory and LC style is completed by long short-term memory (LSTM). The model training and testing are conducted with the data collected by the driving simulator, and the proposed method expresses better performance in LC trajectory prediction compared with several traditional models. The results of this study can enhance the trajectory prediction accuracy of advanced driving assistance systems (ADASs) and reduce the traffic accidents caused by lane changes.