AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving

• URL: http://arxiv.org/abs/2505.15298v3
• Date: Thu, 12 Jun 2025 06:27:06 GMT
• Title: AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving
• Authors: Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang,
• Abstract summary: Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.<n>We introduce AgentThink, a pioneering unified framework that integrates Chain-of-Thought (CoT) reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
• Score: 28.378854340190973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce AgentThink, a pioneering unified framework that, for the first time, integrates Chain-of-Thought (CoT) reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. AgentThink's core innovations include: (i) Structured Data Generation, by establishing an autonomous driving tool library to automatically construct structured, self-verified reasoning data explicitly incorporating tool usage for diverse driving scenarios; (ii) A Two-stage Training Pipeline, employing Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to equip VLMs with the capability for autonomous tool invocation; and (iii) Agent-style Tool-Usage Evaluation, introducing a novel multi-tool assessment protocol to rigorously evaluate the model's tool invocation and utilization. Experiments on the DriveLMM-o1 benchmark demonstrate AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54%, while markedly improving reasoning quality and consistency. Furthermore, ablation studies and robust zero-shot/few-shot generalization experiments across various benchmarks underscore its powerful capabilities. These findings highlight a promising trajectory for developing trustworthy and tool-aware autonomous driving models.

Related papers

• AutoTIR: Autonomous Tools Integrated Reasoning via Reinforcement Learning [17.086082843274003]
  Large Language Models (LLMs) evolve into powerful Large Reasoning Models (LRMs)<n>Tool-Integrated Reasoning (TIR) further extends their capabilities by incorporating external tools.<n>Inspired by the human ability to adaptively select tools, we introduce AutoTIR, a reinforcement learning framework.
  arXiv  Detail & Related papers  (2025-07-29T14:12:28Z)
• DriveAgent-R1: Advancing VLM-based Autonomous Driving with Hybrid Thinking and Active Perception [25.389702138137217]
  Vision-Language Models (VLMs) are advancing autonomous driving, yet their potential is constrained by decision-making and passive perception.<n>We introduce DriveAgent-R1 to tackle these challenges in high-level behavioral decision-making.<n>DriveAgent-R1 features two core innovations: a Hybrid-Thinking framework that adaptively switches between efficient text-based and in-depth tool-based reasoning, and an Active Perception mechanism with a vision toolkit to proactively resolve uncertainties.
  arXiv  Detail & Related papers  (2025-07-28T14:33:15Z)
• Acting Less is Reasoning More! Teaching Model to Act Efficiently [87.28134636548705]
  Tool-integrated reasoning augments large language models with the ability to invoke external tools to solve tasks.<n>Current approaches typically optimize only for final correctness without considering the efficiency or necessity of external tool use.<n>We propose a framework that encourages models to produce accurate answers with minimal tool calls.<n>Our approach reduces tool calls by up to 68.3% and improves tool productivity by up to 215.4%, while maintaining comparable answer accuracy.
  arXiv  Detail & Related papers  (2025-04-21T05:40:05Z)
• ReasonDrive: Efficient Visual Question Answering for Autonomous Vehicles with Reasoning-Enhanced Small Vision-Language Models [9.316712964093506]
  Vision-language models (VLMs) show promise for autonomous driving but often lack transparent reasoning capabilities that are critical for safety.<n>We investigate whether explicitly modeling reasoning during fine-tuning enhances VLM performance on driving decision tasks.
  arXiv  Detail & Related papers  (2025-04-14T23:16:07Z)
• OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning [68.45848423501927]
  We propose a holistic vision-language dataset that aligns agent models with 3D driving tasks through counterfactual reasoning.<n>Our approach enhances decision-making by evaluating potential scenarios and their outcomes, similar to human drivers considering alternative actions.
  arXiv  Detail & Related papers  (2025-04-06T03:54:21Z)
• RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving [10.984203470464687]
  Vision-language models (VLMs) often suffer from limitations such as inadequate spatial perception and hallucination.<n>We propose a retrieval-augmented decision-making (RAD) framework to enhance VLMs' capabilities to reliably generate meta-actions in autonomous driving scenes.<n>We fine-tune VLMs on a dataset derived from the NuScenes dataset to enhance their spatial perception and bird's-eye view image comprehension capabilities.
  arXiv  Detail & Related papers  (2025-03-18T03:25:57Z)
• DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding [76.3876070043663]
  We propose DriveLMM-o1, a dataset and benchmark designed to advance step-wise visual reasoning for autonomous driving.<n>Our benchmark features over 18k VQA examples in the training set and more than 4k in the test set, covering diverse questions on perception, prediction, and planning.<n>Our model achieves a +7.49% gain in final answer accuracy, along with a 3.62% improvement in reasoning score over the previous best open-source model.
  arXiv  Detail & Related papers  (2025-03-13T17:59:01Z)
• Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
  We propose MeCo, an adaptive decision-making strategy for external tool use.<n>MeCo captures high-level cognitive signals in the representation space, guiding when to invoke tools.<n>Our experiments show that MeCo accurately detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.
  arXiv  Detail & Related papers  (2025-02-18T15:45:01Z)
• OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning [68.45848423501927]
  We propose a holistic vision-language dataset that aligns agent models with 3D driving tasks through counterfactual reasoning.<n>Our approach enhances decision-making by evaluating potential scenarios and their outcomes, similar to human drivers considering alternative actions.
  arXiv  Detail & Related papers  (2024-05-02T17:59:24Z)
• AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning [54.47116888545878]
  AutoAct is an automatic agent learning framework for QA. It does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models.
  arXiv  Detail & Related papers  (2024-01-10T16:57:24Z)
• Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving [38.28159034562901]
  Reason2Drive is a benchmark dataset with over 600K video-text pairs. We characterize the autonomous driving process as a sequential combination of perception, prediction, and reasoning steps. We introduce a novel aggregated evaluation metric to assess chain-based reasoning performance in autonomous systems.
  arXiv  Detail & Related papers  (2023-12-06T18:32:33Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model [84.29836263441136]
  This study introduces DriveGPT4, a novel interpretable end-to-end autonomous driving system based on multimodal large language models (MLLMs) DriveGPT4 facilitates the interpretation of vehicle actions, offers pertinent reasoning, and effectively addresses a diverse range of questions posed by users.
  arXiv  Detail & Related papers  (2023-10-02T17:59:52Z)

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.