CVPR 2023 - Planning-oriented Autonomous Driving

In this episode we discuss Planning-oriented Autonomous Driving by Authors: - Yihan Hu - Jiazhi Yang - Li Chen - Keyu Li - Chonghao Sima - Xizhou Zhu - Siqi Chai - Senyao Du - Tianwei Lin - Wenhai Wang - Lewei Lu - Xiaosong Jia - Qiang Liu - Jifeng Dai - Yu Qiao - Hongyang Li Affiliations: - Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, and Xiaosong Jia: OpenDriveLab and OpenGVLab, Shanghai AI Laboratory - Siqi Chai, Senyao Du, Tianwei Lin, and Qiang Liu: Wuhan University - Wenhai Wang and Hongyang Li: OpenDriveLab and OpenGVLab, Shanghai AI Laboratory (†Project lead) - Lewei Lu: SenseTime Research. The paper discusses how current autonomous driving systems use standalone modules or a multi-task paradigm, which can lead to errors or poor task coordination. The authors propose a framework called Unified Autonomous Driving (UniAD) that prioritizes tasks based on their contribution to planning and incorporates full-stack driving tasks in one network. They tested UniAD on the nuScenes benchmark and showed it outperformed previous state-of-the-art methods in all aspects. The code and models are publicly available.