Learning Autonomous Driving Tasks via Human Feedbacks with Large Language Models (2024)

Abstract: Traditional autonomous driving systems have mainly focused on making driving decisions without human interaction, overlooking human-like decision-making and human preference required in complex traffic scenarios. To bridge this gap, we introduce a novel framework leveraging Large Language Models (LLMs) for learning human-centered driving decisions from diverse simulation scenarios and environments that incorporate human feedback. Our contributions include a GPT-4-based programming planner that integrates seamlessly with the existing CARLA simulator to understand traffic scenes and react to human instructions. Specifically, we build a human-guided learning pipeline that incorporates human driver feedback directly into the learning process and stores optimal driving programming policy using Retrieval Augmented Generation (RAG). Impressively, our programming planner, with only 50 saved code snippets, can match the performance of baseline extensively trained reinforcement learning (RL) models. Our paper highlights the potential of an LLM-powered shared-autonomy system, pushing the frontier of autonomous driving system development to be more interactive and intuitive.

## introduction autonomous driving has the potential to revolutionize transportation and urban mobility and has been a primary focus of research and development for the past two decades @xcite . traditional development of autonomous driving has primarily focused on achieving perfect black box safe navigation, and the role of humans in the system is often minimized, due to the doubts about the feasibility and benefits of integrating human and autonomous driving systems collaboratively @xcite . however, the human-like decision is still an important factor in designing autonomous driving systems. human instruction: i saw the news that there is congestion ahead. let's take a left at the next light and re-navigate to my destination. ## conclusion in this paper, we proposed a human-centered autonomous driving learning pipeline with codellms supported by retrieval-augmented generation (rag). our method exhibits strong learning abilities to embed human driving knowledge and preference into the decision-making process of autonomous driving. we conducted interactive experiments in the carla autonomous driving simulator by letting experienced human drivers provide feedback. after learning with human feedback, our programming planner achieves much better performance than zero-shot and few-shot chain-of-thought baselines. more importantly, our framework can perform compelling results using only 50 committed code snippets in the knowledge database for rag without any parameter updates. although our framework has shown promising results in the current setting, there are still some limitations. future improvements could focus on a better personalization and understanding of human behavior in real-world scenarios.