Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework

• URL: http://arxiv.org/abs/2409.12812v2
• Date: Sun, 22 Sep 2024 09:31:58 GMT
• Title: Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
• Abstract summary: 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.
• Score: 79.088116316919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: At present, 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. Cooperative driving leverages the connectivity ability of CAVs to achieve synergies greater than the sum of their parts, making it a promising approach to improving CAV performance in complex scenarios. However, the lack of interaction and continuous learning ability limits current cooperative driving to single-scenario applications and specific Cooperative Driving Automation (CDA). To address these challenges, this paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework, to achieve all-scenario and all-CDA. First, since Large Language Models(LLMs) are not adept at handling mathematical calculations, an environment module is introduced to update vehicle positions based on semantic decisions, thus avoiding potential errors from direct LLM control of vehicle positions. Second, based on the four levels of CDA defined by the SAE J3216 standard, we propose a Chain-of-Thought (COT) based reasoning module that includes state perception, intent sharing, negotiation, and decision-making, enhancing the stability of LLMs in multi-step reasoning tasks. Centralized conflict resolution is then managed through a conflict coordinator in the reasoning process. Finally, by introducing a memory module and employing retrieval-augmented generation, CAVs are endowed with the ability to learn from their past experiences. We validate the proposed CoDrivingLLM through ablation experiments on the negotiation module, reasoning with different shots experience, and comparison with other cooperative driving methods.

Related papers

• CoLMIN: LLM-based Multi-Decision Path Negotiation for Cooperative Autonomous Driving [20.064328696931096]
  CoLMIN aims to achieve stable and high-quality consensus in cooperative autonomous driving.<n>It consists of three key components: (i) an LLM-based Multi-Intent Negotiation module (LMin), which adopts a Negotiator-Evaluator paradigm and generates multiple candidate driving intentions for joint evaluation; (ii) an Evaluation-based Shallow Reflection Module (ESRM), which analyzes negotiation outcomes and provides feedback to guide subsequent negotiations; and (iii) an LLM-based Deep Reflection Module (LDRM), which performs long-term reflection over negotiation histories to mitigate cognitive fixation and prevent the system from converging to suboptimal
  arXiv  Detail & Related papers  (2026-09-04T07:03:29Z)
• A Collaborative Multi-Modality Interaction for VLA-based End-to-End Autonomous Driving [96.81243316566764]
  Vision-Language-Action (VLA) models have emerged as a powerful paradigm for end-to-end autonomous driving.<n>We propose a robust VLA-based end-to-end autonomous driving system that combines multi-modality interaction with multi-trajectory planning and optimization.
  arXiv  Detail & Related papers  (2026-08-21T09:06:16Z)
• CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models [4.009780255195637]
  We present Cooperative Multi-agent Unified Driving with Reasoning (CMU-Drive), a closed-loop end-to-end benchmark for evaluating cooperative autonomous driving.<n>We propose Vehicle-to-Vehicle Vision-Language-Action (V2V-VLA), a cooperative VLA model that integrates cooperative driving into a single forward pass.
  arXiv  Detail & Related papers  (2026-08-07T09:00:39Z)
• MIND-CAVs: Multi-Intelligence Negotiation and Decision System for CAVs based on Intent-Driven Autonomy [5.382627382708821]
  MIND-CAVs is a framework for connected autonomous vehicles (CAVs) based on intent-driven autonomy.<n>Each vehicle abstracts raw sensor observations into structured intent representations, exchanges them over V2X links, and receives globally consistent coordination plans from roadside edge servers.<n>We implement MIND-CAVs in a CARLA-based AI-in-the-loop platform and evaluate it in multi-lane highway scenarios.
  arXiv  Detail & Related papers  (2026-07-16T07:46:34Z)
• Multi-Agent Embodied Autonomous Driving: From V2X Information Exchange to Shared World Models [16.06080006560829]
  Autonomous driving is shifting from isolated vehicle intelligence toward multi-agent embodied systems that share perception, infer intent, and coordinate action under uncertainty.<n>We review more than 380 publications spanning vehicle-to-everything (V2X) communication, collaborative perception, inter-agent cognition, cooperative planning, end-to-end cooperative driving, and simulation and data engines for closed-loop validation.
  arXiv  Detail & Related papers  (2026-06-11T19:10:13Z)
• Large Multimodal Models for Embodied Intelligent Driving: The Next Frontier in Self-Driving? [68.82027978227008]
  This article introduces a novel semantics and policy dual-driven hybrid decision framework to tackle this challenge.<n>The framework merges LMMs for semantic understanding and cognitive representation, and deep reinforcement learning (DRL) for real-time policy optimization.<n>Case study is conducted experimentally to validate the performance superiority of our framework in completing lane-change planning task.
  arXiv  Detail & Related papers  (2026-01-13T11:05:12Z)
• CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine [73.74077186298523]
  CoReVLA is a continual learning framework for autonomous driving.<n>It improves the performance in long-tail scenarios through a dual-stage process of data Collection and behavior Refinement.<n>CoReVLA achieves a Driving Score (DS) of 72.18 and a Success Rate (SR) of 50%, outperforming state-of-the-art methods by 7.96 DS and 15% SR under long-tail, safety-critical scenarios.
  arXiv  Detail & Related papers  (2025-09-19T13:25:56Z)
• AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving [37.260140808367716]
  We propose AutoDrive-R$2$, a novel VLA framework that enhances both reasoning and self-reflection capabilities of autonomous driving systems.<n>We first propose an innovative CoT dataset named nuScenesR$2$-6K for supervised fine-tuning.<n>We then employ the Group Relative Policy Optimization (GRPO) algorithm within a physics-grounded reward framework to ensure reliable smoothness and realistic trajectory planning.
  arXiv  Detail & Related papers  (2025-09-02T04:32:24Z)
• HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways [6.838130893718755]
  A cooperative control strategy based on connected and automated vehicles (CAVs) is a fundamental solution to this problem.<n>While CAVs are not fully widespread, it is necessary to propose a hierarchical cooperative on-ramp merging control (HCOMC) framework for heterogeneous traffic flow.<n>This paper proposes a HCOMC framework, consisting of a hierarchical cooperative planning model based on the modified virtual vehicle model, a discretionary lane-changing model based on game theory, and a multi-dominated optimization model.
  arXiv  Detail & Related papers  (2025-07-15T18:01:29Z)
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving [49.07731497951963]
  ReCogDrive is a novel Reinforced Cognitive framework for end-to-end autonomous driving.<n>We introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers.<n>We then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner.
  arXiv  Detail & Related papers  (2025-06-09T03:14:04Z)
• CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous Driving [28.101283061232472]
  Vehicle-to-vehicle (V2V) cooperative autonomous driving holds great promise for improving safety. Traditional cooperative methods are constrained by rigid collaboration protocols and limited generalization to unseen interactive scenarios. We propose CoLMDriver, the first full-pipeline LLM-based cooperative driving system.
  arXiv  Detail & Related papers  (2025-03-11T17:58:42Z)
• TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning [61.33599727106222]
  TeLL-Drive is a hybrid framework that integrates a Teacher LLM to guide an attention-based Student DRL policy. A self-attention mechanism then fuses these strategies with the DRL agent's exploration, accelerating policy convergence and boosting robustness.
  arXiv  Detail & Related papers  (2025-02-03T14:22:03Z)
• CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic [11.682456863110767]
  CoMAL is a framework designed to address the mixed-autonomy traffic problem by collaboration among autonomous vehicles to optimize traffic flow. CoMAL is built upon large language models, operating in an interactive traffic simulation environment.
  arXiv  Detail & Related papers  (2024-10-18T10:53:44Z)
• SPformer: A Transformer Based DRL Decision Making Method for Connected Automated Vehicles [9.840325772591024]
  We propose a CAV decision-making architecture based on transformer and reinforcement learning algorithms. A learnable policy token is used as the learning medium of the multi-vehicle joint policy. Our model can make good use of all the state information of vehicles in traffic scenario.
  arXiv  Detail & Related papers  (2024-09-23T15:16:35Z)
• Making Large Language Models Better Planners with Reasoning-Decision Alignment [70.5381163219608]
  We motivate an end-to-end decision-making model based on multimodality-augmented LLM. We propose a reasoning-decision alignment constraint between the paired CoTs and planning results. We dub our proposed large language planners with reasoning-decision alignment as RDA-Driver.
  arXiv  Detail & Related papers  (2024-08-25T16:43:47Z)
• KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models [15.951550445568605]
  Large language models (LLMs) as autonomous agents offer a novel avenue for tackling real-world challenges through a knowledge-driven manner. We propose the KoMA framework consisting of multi-agent interaction, multi-step planning, shared-memory, and ranking-based reflection modules.
  arXiv  Detail & Related papers  (2024-07-19T12:13:08Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  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.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• 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)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections [7.860567520771493]
  Cooperative coordination at unsignalized road intersections aims to improve the safety driving traffic throughput for connected and automated vehicles. We introduce a model-free Markov Decision Process (MDP) and tackle it by a Twin Delayed Deep Deterministic Policy (TD3)-based strategy in the deep reinforcement learning framework. We show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve control in the realistic continuous flow.
  arXiv  Detail & Related papers  (2022-05-03T02:56:02Z)
• A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles [21.132777568170702]
  We design an information-sharing-based reinforcement learning framework for connected autonomous vehicles. We show that our approach can improve the CAV system's efficiency in terms of average velocity and comfort. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.
  arXiv  Detail & Related papers  (2020-03-09T19:15:30Z)

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.