Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving

• URL: http://arxiv.org/abs/2402.16036v1
• Date: Sun, 25 Feb 2024 09:28:20 GMT
• Title: Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
• Authors: Hanyi Yu, Shuning Huo, Mengran Zhu, Yulu Gong, Yafei Xiang
• Abstract summary: In March 2016, a Google self-driving car was involved in a minor collision with a bus. This paper introduces a deep learning-based prediction method for autonomous driving lane change behavior. It aims to facilitate safe lane changes and thereby improve road safety.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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