Empowering Autonomous Driving with Large Language Models: A Safety Perspective

• URL: http://arxiv.org/abs/2312.00812v4
• Date: Fri, 22 Mar 2024 17:29:01 GMT
• Title: Empowering Autonomous Driving with Large Language Models: A Safety Perspective
• Authors: Yixuan Wang, Ruochen Jiao, Sinong Simon Zhan, Chengtian Lang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu,
• Abstract summary: This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
• Score: 82.90376711290808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of the deep neural networks within the AD system, particularly in out-of-distribution and uncertain data. To this end, this paper explores the integration of Large Language Models (LLMs) into AD systems, leveraging their robust common-sense knowledge and reasoning abilities. The proposed methodologies employ LLMs as intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning, for enhancing driving performance and safety. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine. Demonstrating superior performance and safety metrics compared to state-of-the-art approaches, our approach shows the promising potential for using LLMs for autonomous vehicles.

Related papers

• Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios [32.16984263644299]
  Large Language Models (LLMs) can generate valuable data for safety measures, but often exhibit distributional biases. We propose a novel framework that integrates active learning with clustering to guide LLM generation. Our results show that the proposed framework produces a more representative set of safety scenarios without requiring prior knowledge of the underlying data distribution.
  arXiv  Detail & Related papers  (2024-10-14T21:48:14Z)
• Towards Inference-time Category-wise Safety Steering for Large Language Models [3.712541089289745]
  Large language models (LLMs) have seen unprecedented advancements in capabilities and applications across a variety of use-cases. The fragile nature of LLMs warrants additional safety steering steps via training-free, inference-time methods. Unlike recent inference-time safety steering works, in this paper we explore safety steering of LLM outputs using category-specific steering vectors.
  arXiv  Detail & Related papers  (2024-10-02T02:02:06Z)
• Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
  Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory. This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
  arXiv  Detail & Related papers  (2024-09-19T14:36:00Z)
• Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events [5.233512464561313]
  Multimodal Large Language Models (MLLMs) offer a novel approach by integrating textual, visual, and audio modalities. Our framework leverages the reasoning power of MLLMs, directing their output through context-specific prompts. Preliminary results demonstrate the framework's potential in zero-shot learning and accurate scenario analysis.
  arXiv  Detail & Related papers  (2024-06-19T23:50:41Z)
• A Superalignment Framework in Autonomous Driving with Large Language Models [2.650382010271]
  Large language models (LLMs) and multi-modal large language models (MLLMs) are extensively used in autonomous driving. Despite their importance, the security aspect of LLMs in autonomous driving remains underexplored. This research introduces a novel security framework for autonomous vehicles, utilizing a multi-agent LLM approach.
  arXiv  Detail & Related papers  (2024-06-09T05:26:38Z)
• LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning [65.86754998249224]
  We develop a novel hybrid planner that leverages a conventional rule-based planner in conjunction with an LLM-based planner. Our approach navigates complex scenarios which existing planners struggle with, produces well-reasoned outputs while also remaining grounded through working alongside the rule-based approach.
  arXiv  Detail & Related papers  (2023-12-30T02:53:45Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [87.1164964709168]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving [100.57791628642624]
  We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
  arXiv  Detail & Related papers  (2022-03-04T16:14:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.