How to Cite
Abstract
Traditional autonomy in vehicles can be divided into perception, prediction, and policy decision-making—the vehicle first perceives the surroundings through various sensors, such as cameras, LIDAR, and radar, tracks relevant objects based on sensor information, uses the prediction model to predict the future position and behaviors of moving obstacles, and then outputs policy decisions to control the vehicle movement. In recent years, thanks to the rapid development of deep learning, autonomous vehicle navigation has evolved from the original architecture into an end-to-end manner, where learning-based methods play the dominant role in autonomous navigation [1].
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