HybridDriveVLA：面向自动驾驶的视觉思维链推理与思维树评估的视觉-语言-动作模型
HybridDriveVLA: Vision-Language-Action Model with Visual CoT reasoning and ToT Evaluation for Autonomous Driving
将视觉思维链（V-CoT）用于预测未来场景作为目标，并结合思维树（ToT）对候选轨迹从安全、进度、舒适度多维度评分，为自动驾驶中的轨迹规划提供可解释的推理框架。
原文依据 V-Cot reasoning anticipates future scenes, which serve as goals for ToT-evaluation.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉-语言-动作（VLA）模型正成为自动驾驶领域的重要技术，因其复杂的推理能力和可解释性而受到认可。然而，传统的VLA模型通常依赖图像到文本的思维链（CoT）推理，将连续的视觉场景转换为文本符号，从而未能充分利用视觉信息中的空间上下文。现有的使用VLA模型的自动驾驶系统仅根据给定指令和多个方面预测单条路径点序列作为轨迹。然而，我们建议对每条路径点序列进行评估，以揭示相应方面的重要性。我们提出HybridDriveVLA，一种集成了视觉思维链（V-CoT）推理和受思维树（ToT）启发的路径点评估（ToT-evaluation）的VLA模型。V-CoT推理预测未来场景，这些场景作为ToT-evaluation的目标。ToT-evaluation基于安全性、进度和舒适度三个方面生成路径点并对其进行评分。基于这三个方面的路径点累积得分最高者为最优。据我们所知，我们是首个在VLA模型中统一集成CoT和ToT方法的工作。实验结果表明，HybridDriveVLA在nuScenes基准上在舒适度、进度和安全性指标上取得了强劲性能，平均碰撞率为0.17%，优于传统VLA模型。
英文原文摘要 1480 字符
Vision-Language-Action (VLA) models are emerging as an important technology in autonomous driving, recognized for their sophisticated reasoning and interpretability. However, traditional VLA models often rely on image-to-text with Chain-of-Thought (CoT) reasoning, which converts sequential visual scenes into textual symbols, thereby under-utilizing spatial context in visual information. Existing autonomous driving systems using VLA models predict only a single sequence of waypoints as a trajectory considering a given command and multiple aspects. However, we suggest evaluating each sequence of waypoints to reveal the importance of the corresponding aspect. We introduce HybridDriveVLA, a VLA model that integrates visual Chain-of-Thought (V-Cot) reasoning and a proposed Tree-of-Thought (ToT)-inspired waypoint evaluation (ToT-evaluation). V-Cot reasoning anticipates future scenes, which serve as goals for ToT-evaluation. The ToT-evaluation generates and scores waypoints based each on safety, progress, and comfort aspects. The highest cumulated score of the waypoints based on the three aspects is optimal. To the best of our knowledge, we are the first to propose a unified method integrating both a CoT and a ToT approach in a VLA model. Experimental results demonstrated that HybridDriveVLA achieved strong performance on comfort, progress, and safety metrics with and average collision rate of 0.17% on the nuScenes benchmark, outperforming traditional VLA models.