Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
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 .
Code (1)
Tasks
Autonomous DrivingCommon Sense ReasoningLanguage ModelingLanguage ModellingLarge Language ModelMemorizationSimilar Papers 제목 키워드 기반
DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models
Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent. Commonly used rule-based drivi…
Autonomous DrivingSelf-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles
This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose a…
Autonomous VehiclesLearning Accurate, Comfortable and Human-like Driving
Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but also similar to how human drivers would. This is especially true when autonomous and human-driven vehicles need to share the s…
Autonomous VehiclesNavigateA Language Agent for Autonomous Driving
Human-level driving is an ultimate goal of autonomous driving. Conventional approaches formulate autonomous driving as a perception-prediction-planning framework, yet their systems do not capitalize on the inherent reaso…
Autonomous DrivingCommon Sense ReasoningDecision MakingFew-Shot Learning+2Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios
Merge scenarios on highway are often challenging for autonomous driving, due to its lack of sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow. This, as a result, may impose s…
Autonomous DrivingDecision Making