Cross-Scenario Maneuver Decision with Adaptive Perception for Autonomous Driving

Yuan Fu, Shuncheng Liu, Yuyang Xia, Fangda Guo, Kai Zheng

Abstract: Autonomous driving is a rapidly advancing field that promises to revolutionize the transportation industry through an intelligent perception-and-decision paradigm. Despite decades of research, existing methods are limited in adapting to complex scenarios or expanding to unseen situations, which pose significant challenges to the development of autonomous driving. Inspired by the process of human learning to drive, autonomous vehicles can prioritize developing driving capabilities in basic scenarios and then extending the atomic abilities to more complex scenarios. To this end, we proposed a perception-and-decision framework, called ATEND, which consists of an adaptive perception module and a maneuver decision module. Specifically, the perception module based on Variational Autoencoder is proposed to map perceptual data of complex scenarios into basic scenarios. Then the reinforcement learning-based decision module can make high-level decisions in transformed scenarios. Once ATEND learns to drive in basic scenarios, it can achieve safe and efficient driving in real scenarios without additional training. Extensive experiments in different traffic scenarios evidence that the proposed framework advances the state of the art in terms of both macroscopic and microscopic effectiveness.