Towards Knowledge-driven Autonomous Driving
CoRR(2023)
摘要
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}.
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