Machine Learning-Based Vehicle Intention Trajectory Recognition and
  Prediction for Autonomous Driving
Hanyi Yu Shuning Huo Mengran Zhu Yulu Gong Yafei Xiang
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.