Automated Lane Change Behavior Prediction and Environmental Perception
  Based on SLAM Technology
Han Lei Baoming Wang Zuwei Shui Peiyuan Yang Penghao Liang
In addition to environmental perception sensors such as cameras, radars, etc.
in the automatic driving system, the external environment of the vehicle is
perceived, in fact, there is also a perception sensor that has been silently
dedicated in the system, that is, the positioning module. This paper explores
the application of SLAM (Simultaneous Localization and Mapping) technology in
the context of automatic lane change behavior prediction and environment
perception for autonomous vehicles. It discusses the limitations of traditional
positioning methods, introduces SLAM technology, and compares LIDAR SLAM with
visual SLAM. Real-world examples from companies like Tesla, Waymo, and Mobileye
showcase the integration of AI-driven technologies, sensor fusion, and SLAM in
autonomous driving systems. The paper then delves into the specifics of SLAM
algorithms, sensor technologies, and the importance of automatic lane changes
in driving safety and efficiency. It highlights Tesla's recent update to its
Autopilot system, which incorporates automatic lane change functionality using
SLAM technology. The paper concludes by emphasizing the crucial role of SLAM in
enabling accurate environment perception, positioning, and decision-making for
autonomous vehicles, ultimately enhancing safety and driving experience.