Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
<|reference_start|>Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving: In recent years, the expansion of internet technology and advancements in automation have brought significant attention to autonomous driving technology. Major automobile manufacturers, including Volvo, Mercedes-Benz, and Tesla, have progressively introduced products ranging from assisted-driving vehicles to semi-autonomous vehicles. However, this period has also witnessed several traffic safety incidents involving self-driving vehicles. For instance, in March 2016, a Google self-driving car was involved in a minor collision with a bus. At the time of the accident, the autonomous vehicle was attempting to merge into the right lane but failed to dynamically respond to the real-time environmental information during the lane change. It incorrectly assumed that the approaching bus would slow down to avoid it, leading to a low-speed collision with the bus. This incident highlights the current technological shortcomings and safety concerns associated with autonomous lane-changing behavior, despite the rapid advancements in autonomous driving technology. Lane-changing is among the most common and hazardous behaviors in highway driving, significantly impacting traffic safety and flow. Therefore, lane-changing is crucial for traffic safety, and accurately predicting drivers' lane change intentions can markedly enhance driving safety. This paper introduces a deep learning-based prediction method for autonomous driving lane change behavior, aiming to facilitate safe lane changes and thereby improve road safety.<|reference_end|>
@article{yu2024machine,
    title={Machine Learning-Based Vehicle Intention Trajectory Recognition and
  Prediction for Autonomous Driving},
    author={Hanyi Yu, Shuning Huo, Mengran Zhu, Yulu Gong, Yafei Xiang},
    journal={arXiv preprint arXiv:2402.16036},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16036},
    primaryClass={cs.RO cs.CV cs.LG}
}