Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
This addresses safety concerns for autonomous vehicles, particularly in highway driving, but appears incremental as it builds on existing deep learning approaches for a known bottleneck.
The paper tackles the problem of predicting lane change intentions in autonomous driving to enhance safety, introducing a deep learning-based method that aims to improve road safety by facilitating safer lane changes.
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.