Abstract
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.
Abstract (translated)
除了自动驾驶系统中的环境感知传感器（如摄像头、雷达等）外，还感知车辆外部的环境，实际上，系统中还有一个静默安装的感知传感器，即定位模块。本文探讨了在自动驾驶车辆中应用SLAM（同时定位与映射）技术的应用，特别是在自动变道行为预测和环境感知方面。它讨论了传统定位方法的局限性，介绍了SLAM技术，并比较了LIDAR SLAM与视觉SLAM。特斯拉、Waymo和Mobileye等公司的实际案例展示了AI驱动技术、传感器融合和SLAM在自动驾驶系统中的应用。接着，文章深入探讨了SLAM算法的具体细节、传感器技术以及自动变道在驾驶安全与效率中的重要性。最后，文章强调了SLAM在使自动驾驶车辆准确感知环境、定位和做出决策方面的重要性，从而提高了安全性和驾驶体验。
URL
https://arxiv.org/abs/2404.04492
