A unified modeling framework for lane change intention recognition and vehicle status prediction

Abstract

Accurately detecting and predicting Lane Change (LC) processes of human-driven vehicles can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This paper focuses on LC processes, first developing a Temporal Convolutional Network (TCN) with an attention mechanism (ATM) model to recognize LC intention. Then, considering the intrinsic relationship among output variables, the Multi-Task Learning (MTL) framework is employed to simultaneously predict multiple LC vehicle status indicators. Furthermore, a unified modeling framework for LC intention recognition and driving status prediction (LC-IR-SP) is developed. The results indicate that the classification accuracy of LC intention was improved from 95.83% to 98.20% when incorporating the ATM into the TCN model. For LC vehicle status prediction issues, Pearson's correlation coefficient indicates that metrics extracted from LC processes show stronger correlation than those extracted from Lane-keeping processes. Consequently, three multi-tasking learning models are constructed based on the MTL framework. The results indicate that the MTL with Long Short-Term Memory (MTL-LSTM) model outperforms the MTL with TCN (MTL-TCN) and MTL with TCN-ATM (MTL-TCN-ATM) models. Compared to the corresponding single-task model, the MTL-LSTM model demonstrates an average decrease of 26.04% in MAE and 25.19% in RMSE. The LC-IR-SP model developed holds great potential in enhancing autonomous vehicles' perception and prediction capabilities, such as identifying LC behaviors, calculating real-time traffic conflict indices, and improving vehicle control strategies.