A Unified Approach to Lane Change Intention Recognition and Driving Status Prediction through TCN-LSTM and Multi-Task Learning Models

Lane change (LC) is a continuous and complex operation process. Accurately detecting and predicting LC processes can help traffic participants better understand their surrounding environment, recognize potential LC safety hazards, and improve traffic safety. This present paper focuses on LC processes, developing an LC intention recognition (LC-IR) model and an LC status prediction (LC-SP) model. A novel ensemble temporal convolutional network with Long Short-Term Memory units (TCN-LSTM) is first proposed to capture long-range dependencies in sequential data. Then, three multi-task models (MTL-LSTM, MTL-TCN, MTL-TCN -LSTM) are developed to capture the intrinsic relationship among output indicators. Furthermore, a unified modeling framework for LC intention recognition and driving status prediction (LC-IR-SP) is developed. To validate the performance of the proposed models, a total number of 1023 vehicle trajectories is extracted from the CitySim dataset. The Pearson coefficient is employed to determine the related indicators. The results indicate that using150 frames as input length, the TCN-LSTM model with 96.67% accuracy outperforms TCN and LSTM models in LC intention classification and provides more balanced results for each class. Three proposed multi-tasking learning models provide markedly increased performance compared to corresponding single-task models, with an average reduction of 24.24% and 22.86% in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), respectively. The developed LC-IR-SP model has promising applications for autonomous vehicles to identity lane change behaviors, calculate a real-time traffic conflict index and improve vehicle control strategies.