Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles

• URL: http://arxiv.org/abs/2304.13732v2
• Date: Wed, 19 Jul 2023 18:18:55 GMT
• Title: Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles
• Authors: Renteng Yuan, Mohamed Abdel-Aty, Xin Gu, Ou Zheng, Qiaojun Xiang
• Abstract summary: Lane change processes 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 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.
• Score: 0.47248250311484113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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