Drive Like a Human: Rethinking Autonomous Driving with Large Language
  Models
Daocheng Fu Xin Li Licheng Wen Min Dou Pinlong Cai Botian Shi Yu Qiao
In this paper, we explore the potential of using a large language model (LLM)
to understand the driving environment in a human-like manner and analyze its
ability to reason, interpret, and memorize when facing complex scenarios. We
argue that traditional optimization-based and modular autonomous driving (AD)
systems face inherent performance limitations when dealing with long-tail
corner cases. To address this problem, we propose that an ideal AD system
should drive like a human, accumulating experience through continuous driving
and using common sense to solve problems. To achieve this goal, we identify
three key abilities necessary for an AD system: reasoning, interpretation, and
memorization. We demonstrate the feasibility of employing an LLM in driving
scenarios by building a closed-loop system to showcase its comprehension and
environment-interaction abilities. Our extensive experiments show that the LLM
exhibits the impressive ability to reason and solve long-tailed cases,
providing valuable insights for the development of human-like autonomous
driving. The related code are available at
https://github.com/PJLab-ADG/DriveLikeAHuman .