Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving

📄 arXiv: 2606.22913v1 📥 PDF

作者: Zisheng Chen, Yuping Qiu, Jianhua Han, Tao Tang, Xiuwei Chen, Likui Zhang, Ying-Cong Chen, Hang Xu, Xiaodan Liang

分类: cs.CV, cs.AI

发布日期: 2026-06-22

💡 一句话要点

提出IRR-Drive框架以解决复杂环境下自主驾驶的可靠性问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 自主驾驶 多模态反思 视觉-语言-动作 轨迹生成 场景预测 自适应机制 决策修正

📋 核心要点

  1. 现有自主驾驶方法往往直接生成轨迹，缺乏对未来后果的深入考量，导致在复杂环境中的可靠性不足。
  2. IRR-Drive框架通过生成初步意图和预测未来场景，结合文本和鸟瞰图的双模态反思，增强了决策过程的自我修正能力。
  3. 在NAVSIM基准测试中，IRR-Drive在PDMS和EPDMS任务上均取得了最先进的性能，验证了其自适应反思策略的有效性。

📝 摘要（中文）

近年来，视觉-语言-动作（VLA）模型通过引入推理机制，提升了自主驾驶的可解释性和规划质量。然而，现有方法往往直接生成最终轨迹，而未能充分考虑未来后果，限制了其在复杂动态环境中的可靠性。为此，本文提出了IRR-Drive（Intend, Reflect, Refine），一个自适应多模态反思框架。IRR-Drive首先生成初步文本意图，并预测未来的语义鸟瞰图（BEV）表示，以紧密结合高层推理与物理约束。该框架通过双模态反思空间显式建模预期场景演变，使模型能够在生成最终轨迹前严格自我修正。此外，本文构建了反思导向的训练数据，并设计了自适应反思奖励，使模型能够根据场景复杂性自适应选择推理模式。实验结果表明，IRR-Drive在NAVSIM基准测试中实现了最先进的性能，验证了其多模态反思框架的有效性。

🔬 方法详解

问题定义：本文旨在解决现有自主驾驶方法在复杂动态环境中缺乏对未来后果的考虑，导致的轨迹生成可靠性不足的问题。

核心思路：IRR-Drive框架通过生成初步文本意图和预测未来的BEV表示，结合双模态反思空间，增强了模型的自我修正能力，从而提高决策的可靠性。

技术框架：IRR-Drive的整体架构包括初步意图生成模块、未来场景预测模块和自适应反思机制。模型首先生成初步意图，然后预测未来场景演变，最后通过反思机制进行轨迹修正。

关键创新：IRR-Drive的主要创新在于将自适应反思机制直接集成到规划框架中，使得模型能够根据场景复杂性自适应选择推理模式，从而实现基于场景的决策修正。

关键设计：本文设计了反思导向的训练数据和自适应反思奖励，以平衡规划性能与计算效率。此外，模型的损失函数和网络结构经过精心设计，以支持多模态信息的有效融合。

🖼️ 关键图片

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📊 实验亮点

IRR-Drive在NAVSIM基准测试中表现出色，在PDMS和EPDMS任务上均实现了最先进的性能，具体提升幅度未知，验证了其多模态反思框架的有效性和自适应反思策略的优势。

🎯 应用场景

IRR-Drive框架在自主驾驶领域具有广泛的应用潜力，能够提升自动驾驶系统在复杂和动态环境中的决策能力。该研究的成果可为未来的智能交通系统提供更可靠的技术支持，推动无人驾驶技术的实际落地与应用。

📄 摘要（原文）

Recent Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving by incorporating reasoning for better interpretability and planning quality. However, most existing approaches directly generate the final trajectory without explicitly examining its future consequences, which limits their reliability in complex and dynamic environments. To address this limitation, we propose IRR-Drive (Intend, Reflect, Refine), an adaptive multimodal reflection framework for autonomous driving. Specifically, to tightly couple high-level reasoning with physical constraints, IRR-Drive first generates a preliminary textual intention and anticipates potential interactions by predicting future semantic bird's-eye view (BEV) representations. This dual-modality (Text + BEV) reflection space explicitly models anticipated scene evolution, enabling the model to rigorously self-correct and refine its initial intent before generating the final trajectory. Furthermore, to balance planning performance and computational efficiency, we construct reflection-oriented training data and design an adaptive reflection reward, enabling the model to adaptively select its reasoning mode according to scene complexity. Instead of using reasoning primarily as an auxiliary interpretation, IRR-Drive directly integrates an adaptive reflection mechanism into the planning framework, enabling grounded, decision-aware trajectory correction that is driven by scene complexity. Our method achieves state-of-the-art performance on the NAVSIM benchmark in both PDMS and EPDMS. Extensive experiments demonstrate the effectiveness of our multimodal reflection framework and validate the efficacy of the proposed adaptive reflection strategy.