Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving
Lightweight confidence-aware language model achieves SOTA success in autonomous driving decision-making, with low latency and high robustness.
Key Findings
Methodology
This paper introduces a multi-agent collaborative framework comprising action voting, confidence assessment, and summarization, leveraging explicit Chain-of-Thought (CoT) reasoning to generate high-quality decision demonstrations with confidence annotations. Multiple LLM agents perform scene understanding, key object identification, and action selection, producing diverse reasoning paths. Confidence scores are assigned via dedicated assessment agents, reducing bias and improving reliability. These demonstrations are distilled into a dual-head lightweight language model, which jointly predicts decision probabilities and generates textual rationales. The distillation employs a confidence-aware fine-tuning strategy combined with Retrieval Augmented Generation (RAG), enhancing data efficiency and adaptability. The resulting model supports fast, interpretable multimodal decision-making suitable for resource-constrained autonomous vehicles.
Key Results
- On the nuPlan benchmark, the proposed method achieves success rates of 96.93% in regular scenarios and 89.71% in long-tail scenarios, outperforming many large-model-based approaches. The inference latency remains below 0.1 seconds, demonstrating real-time capability. The multi-agent demonstration improves robustness, especially in complex environments. The combination of confidence assessment and RAG-based distillation significantly enhances data efficiency and generalization, validated through ablation studies.
- The experiments show that the model maintains high decision accuracy and interpretability, with decision probabilities aligning well with actual outcomes. Its performance in diverse scenarios indicates strong generalization, crucial for real-world deployment. The approach effectively balances reasoning depth and inference speed, making it suitable for resource-limited autonomous driving systems.
- Ablation studies confirm that multi-agent cooperation, confidence scoring, and retrieval augmentation each contribute significantly to overall performance. The model's ability to generate reliable confidence estimates and textual explanations enhances trustworthiness, a key requirement for safety-critical applications like autonomous driving.
Significance
This research addresses the critical challenge of deploying large, computationally intensive models in resource-constrained autonomous vehicles. By integrating multi-agent collaboration, explicit reasoning, and confidence-aware distillation into a lightweight model, it achieves a remarkable balance between performance, interpretability, and efficiency. The approach advances the state-of-the-art in safe, reliable decision-making under uncertainty, paving the way for practical, scalable autonomous driving solutions. Its success demonstrates that high-level reasoning and multimodal understanding can be effectively compressed into low-latency models, significantly impacting both academia and industry.
Technical Contribution
The paper introduces a novel multi-agent workflow for generating confidence-annotated decision demonstrations, leveraging explicit Chain-of-Thought reasoning. It innovatively distills these demonstrations into a dual-head lightweight language model that predicts decision probabilities and textual rationales simultaneously. The integration of confidence-aware fine-tuning and Retrieval Augmented Generation (RAG) enhances data efficiency and adaptability. The dual-task training framework ensures the model maintains reasoning capabilities while achieving low inference latency. This work departs from traditional auto-regressive large models, offering a scalable, interpretable, and robust decision-making architecture suitable for real-time autonomous driving.
Novelty
This is the first work to combine multi-agent collaboration with explicit confidence estimation and distillation into a dual-head lightweight model for autonomous driving. The explicit use of Chain-of-Thought reasoning in generating diverse, confidence-annotated decision demonstrations, coupled with RAG-based fine-tuning, sets a new standard for efficient, trustworthy multimodal decision-making. Unlike prior approaches relying solely on large models or rule-based systems, this method achieves high performance with significantly reduced computational overhead, representing a major step forward in scalable autonomous decision systems.
Limitations
- The model's performance may degrade in extremely rare or unpredictable scenarios, such as severe weather or sudden obstacles, due to limited demonstration diversity and reliance on prior knowledge.
- The multi-agent demonstration generation process depends heavily on the quality and variety of the training data, which may limit generalization in unseen environments.
- While inference latency is low, the training process still requires substantial computational resources, and further optimization is needed for deployment on embedded automotive hardware.
Future Work
Future research will focus on enhancing multi-modal perception integration, improving robustness in extreme scenarios, and exploring reinforcement learning for adaptive decision-making. Additionally, efforts will be made to optimize model deployment on embedded systems, reducing energy consumption and latency further. Extending the framework to incorporate online learning and continual adaptation could further improve performance in dynamic real-world environments.
AI Executive Summary
Autonomous driving systems require decision-making modules that are both accurate and efficient, especially in complex urban scenarios. Traditional large language models (LLMs) and multimodal models (MLLMs) possess remarkable reasoning capabilities but are hampered by high computational costs and latency, limiting their practical deployment in resource-constrained vehicles. To overcome this, recent efforts have explored knowledge distillation, multi-agent collaboration, and explicit reasoning mechanisms. Building on these advances, this paper introduces a novel framework that combines multi-agent cooperative reasoning, confidence-aware demonstration generation, and distillation into a lightweight dual-head language model.
The core idea involves multiple LLM agents performing scene understanding, key object identification, and action selection through explicit Chain-of-Thought reasoning, producing diverse decision demonstrations annotated with confidence scores. These demonstrations are then distilled into a compact model that predicts decision probabilities and generates textual rationales simultaneously. The distillation process employs a confidence-aware fine-tuning strategy coupled with Retrieval Augmented Generation (RAG), significantly improving data efficiency and model adaptability.
Experimental results on the nuPlan benchmark demonstrate that the proposed approach achieves state-of-the-art success rates of 96.93% in regular scenarios and 89.71% in long-tail scenarios, with inference latency below 0.1 seconds. This performance surpasses many large-model-based methods while maintaining real-time inference, validating its suitability for resource-limited autonomous vehicles.
The significance of this work lies in its ability to reconcile the need for deep multimodal reasoning with the practical constraints of embedded automotive hardware. By effectively compressing complex decision-making processes into a fast, interpretable model, it paves the way for safer, more reliable autonomous driving. Future directions include integrating richer perception modalities, enhancing robustness in extreme conditions, and optimizing deployment for real-world automotive systems, ultimately bringing scalable, trustworthy autonomous driving closer to reality.
Deep Analysis
Background
自动驾驶技术经历了从规则驱动到学习驱动的演变，深度学习和大模型的引入极大提升了系统智能水平。早期方法依赖手工规则，难以应对复杂场景。近年来，基于深度学习的行为预测和路径规划取得突破，代表性工作如Waymo的行为预测模型和Tesla的自动驾驶系统。大规模预训练模型如GPT-4和多模态模型在理解环境和推理方面展现潜力，但其高计算成本限制了在车载硬件上的应用。多模态融合和知识蒸馏技术逐渐成为研究热点，旨在实现高效、鲁棒的自动驾驶决策。
Core Problem
当前大模型在自动驾驶中的应用面临两个主要瓶颈：一是推理延迟过高，难以满足实时性需求；二是缺乏明确的不确定性表达，影响决策的可信度。虽然已有的多模态推理框架能生成多样化路径，但计算成本巨大，难以在资源有限的车载平台部署。此外，模型在长尾场景中的泛化能力不足，导致系统在极端复杂环境下表现不佳。这些问题限制了大模型在实际自动驾驶中的广泛应用，亟需高效、可信的决策方案。
Innovation
本研究的创新点包括：1）设计多智能体合作流程，生成多样化且置信度标注的决策示范，增强推理的多样性和可信度；2）引入显式链式推理（CoT）机制，提升多模态意图推理的深度和解释性；3）将示范蒸馏到双头轻量模型中，结合置信感知微调和检索增强生成（RAG），实现低延迟、高数据效率的推理能力；4）模型同时预测决策概率和生成文本理由，兼具可解释性和鲁棒性。这些创新突破了传统大模型的高成本限制，为自动驾驶提供了高效可信的决策方案。
Methodology
- �� 多智能体协作：通过多个LLM智能体模拟多样化决策路径，每个智能体执行场景理解、关键对象识别和行动选择三步链式推理，生成多样化决策示范。
- �� 置信度评估：为每个智能体生成的路径赋予置信度，利用单独的置信度评估智能体分析路径正确性，减少偏差。
- �� 示范总结：将多路径示范整合，生成最终决策及理由，结合置信度进行概率归一。
- �� 蒸馏训练：将示范中的推理和置信信息蒸馏到双头轻量模型中，模型同时进行决策概率预测和文本生成。
- �� 微调策略：采用置信感知微调和检索增强生成（RAG），提升模型在有限数据上的适应性和推理可信度。
- �� 运动规划：结合决策概率和置信度，动态筛选候选动作集，利用扩散模型优化轨迹，最终实现高效决策。
Experiments
在nuPlan基准上进行验证，采用成功率和闭环得分作为主要指标。比较对象包括规则驱动、数据驱动、混合和知识驱动方法。模型超参数如置信阈值γc=0.1，推理延迟控制在0.1秒以内。通过多场景测试，验证模型在常规和长尾场景中的鲁棒性。采用AB测试和消融实验，分析多智能体示范、置信评估和微调策略对性能的影响。实验结果显示，本文模型在成功率和鲁棒性方面均优于现有方法，验证了其高效性和实用性。
Results
模型在nuPlan测试集中的成功率达96.93%，长尾场景89.71%，在多项指标中优于大模型和传统方法。推理延迟低于0.1秒，满足实时需求。多智能体示范增强了模型的鲁棒性，置信度评估提升了决策可信度。蒸馏策略显著改善了模型的数据效率和泛化能力，特别在复杂环境中表现优异。整体结果表明，该方法在保证推理速度的同时，达到了SOTA的决策成功率，为自动驾驶系统的实际部署提供了有力支持。
Applications
该模型可直接应用于自动驾驶车辆的决策模块，尤其适合资源受限的平台。其高效、可信的推理能力，有助于提升自动驾驶系统的安全性和鲁棒性。未来可结合多模态感知信息，优化路径规划和交互策略，推动智能驾驶的商业化落地。长远来看，该技术有望实现全场景自主驾驶，降低成本，提升用户体验。
Limitations & Outlook
模型在极端天气或突发事件中可能表现不足，示范多样性受限，泛化能力仍需提升。此外，模型在极端长尾场景中的表现尚未完全优化，未来需结合强化学习和多模态信息融合，增强系统鲁棒性。计算资源需求虽低于大模型，但在极端复杂环境下仍存在性能瓶颈。
Plain Language Accessible to non-experts
想象你在开车，面对复杂的路况和交通情况，你需要快速做出安全合理的决定。传统方法像是用一本厚厚的指南书，内容丰富但太重，难以快速拿出来用。现在，这个研究提出一种轻巧的“智能助手”，它通过模拟多个“司机”的思考过程，生成不同的驾驶建议，并给出每个建议的可信度。然后，它把这些建议浓缩成一个简单的“聪明小助手”，可以在车上快速使用。这个小助手不仅能告诉你哪个动作最安全，还能解释为什么这样选择，就像一个懂事的朋友一样。这样，车辆可以更快、更安全地应对各种复杂情况，就像有一群聪明的朋友在你身边帮忙一样。
ELI14 Explained like you're 14
你知道开车时遇到复杂的路况，要快速决定怎么走吗？以前的方法就像用一本很厚的书，要花很长时间才能找到答案。而这项新技术像是有一群聪明的朋友在你身边，他们每个人都想出不同的建议，比如左转、右转、加速或减速。每个朋友还会告诉你他们觉得这个建议有多靠谱。最后，这些建议会被总结成一个简单的方案，告诉你最安全、最合适的动作，还会解释原因。这个过程就像你有一群聪明的朋友帮你出主意，而且他们还能告诉你他们的想法有多靠谱。这样，你就可以更快、更安全地开车，尤其是在复杂或突发的情况中。
Abstract
Large Language Models (LLMs) and Multimodal LLMs (MLLMs) have demonstrated immense potential in autonomous driving (AD) by offering human-like reasoning and open-world generalization. However, the excessive computational overhead and high inference latency of these massive models severely hinder their deployment in resource-constrained AD systems. To address this challenge, we propose a novel decision-making framework utilizing a lightweight confidence-aware language model, which bridges the gap between complex multimodal intention reasoning and efficient inference. Specifically, we design a multi-agent collaborative workflow, comprising action voting, confidence assessment, and summarization agents, to generate high-quality, confidence-annotated decision demonstrations via explicit Chain-of-Thought (CoT) reasoning. These demonstrations are then distilled into a lightweight language model featuring a dual-head architecture, enabling the joint prediction of decision probabilities and the generation of textual rationales. The distillation is realized via a confidence-aware fine-tuning strategy coupled with Retrieval Augmented Generation (RAG) to enhance the model's adaptability and data efficiency. Comprehensive closed-loop experiments on the nuPlan benchmark demonstrate that our approach achieves state-of-the-art (SOTA) success rates in both regular and long-tail scenarios while maintaining low inference latency.