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.

翻译：近年来，互联网技术的扩展与自动化技术的进步使自动驾驶技术备受关注。沃尔沃、梅赛德斯-奔驰、特斯拉等主要汽车制造商已逐步推出从辅助驾驶到半自动驾驶的产品。然而，这一时期也发生了多起涉及自动驾驶汽车的交通安全事故。例如，2016年3月，一辆谷歌自动驾驶汽车与一辆公交车发生轻微碰撞。事故发生时，该自动驾驶汽车正试图并入右侧车道，但未能根据变道过程中的实时环境信息做出动态响应。它错误地认为后方驶来的公交车会减速避让，从而导致低速碰撞。这一事件揭示了尽管自动驾驶技术发展迅速，但在自主变道行为方面仍存在技术缺陷与安全隐患。变道是高速公路驾驶中最常见且最危险的行为之一，对交通安全和交通流具有显著影响。因此，变道对交通安全至关重要，准确预测驾驶员的变道意图可显著提升驾驶安全性。本文提出一种基于深度学习的自动驾驶变道行为预测方法，旨在实现安全变道，进而提高道路安全水平。