Towards Knowledge-driven Autonomous Driving
Abstract
This paper explores the emerging knowledge-driven autonomous driving technologies. Our investigation highlights the limitations of current autonomous driving systems, in particular their sensitivity to data bias, difficulty in handling long-tail scenarios, and lack of interpretability. Conversely, knowledge-driven methods with the abilities of cognition, generalization and life-long learning emerge as a promising way to overcome these challenges. This paper delves into the essence of knowledge-driven autonomous driving and examines its core components: dataset \& benchmark, environment, and driver agent. By leveraging large language models, world models, neural rendering, and other advanced artificial intelligence techniques, these components collectively contribute to a more holistic, adaptive, and intelligent autonomous driving system. The paper systematically organizes and reviews previous research efforts in this area, and provides insights and guidance for future research and practical applications of autonomous driving. We will continually share the latest updates on cutting-edge developments in knowledge-driven autonomous driving along with the relevant valuable open-source resources at: \url{https://github.com/PJLab-ADG/awesome-knowledge-driven-AD}.
Cite
@article{arxiv.2312.04316,
title = {Towards Knowledge-driven Autonomous Driving},
author = {Xin Li and Yeqi Bai and Pinlong Cai and Licheng Wen and Daocheng Fu and Bo Zhang and Xuemeng Yang and Xinyu Cai and Tao Ma and Jianfei Guo and Xing Gao and Min Dou and Yikang Li and Botian Shi and Yong Liu and Liang He and Yu Qiao},
journal= {arXiv preprint arXiv:2312.04316},
year = {2023}
}