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.

本文介绍了一种基于深度学习的预测方法，用于自动驾驶车辆的变道行为，旨在促进安全的变道操作，从而提高道路安全。