Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles
This work addresses safety and environmental understanding for autonomous vehicles, but it is incremental as it builds on existing methods like TCN and MTL with specific enhancements.
The paper tackled lane change intention recognition and vehicle status prediction for autonomous vehicles by developing a temporal convolutional network with attention and multi-task learning frameworks, achieving a classification accuracy improvement from 96.14% to 98.20% and reducing MAE and RMSE by about 25-26% compared to single-task models.
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 with an attention mechanism (TCN-ATM) model to recognize LC intention. 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 96.14% to 98.20% when incorporating the attention mechanism into the TCN model. For LC vehicle status prediction issues, three multi-tasking learning models are constructed based on MTL framework. The results indicate that the MTL-LSTM model outperforms the MTL-TCN and 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.