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Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles

arXiv 2304.13732 ↗First posted 25 Apr 2023cs.LGcited by 0

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Abstract (arXiv)

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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References · 11

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  7. 2018Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecturecited by 55
  8. 2022CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twinscited by 43
  9. 2021Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Drivingcited by 36
  10. 2022Multi-modal Fusion Technology based on Vehicle Information: A Surveycited by 13
  11. 2021Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Studycited by 7

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