CARE Drive：一种用于评估视觉语言模型在自动驾驶中推理响应性的框架
CARE Drive A Framework for Evaluating Reason-Responsiveness of Vision Language Models in Automated Driving
AI总结：
CARE Drive提出了一种评估视觉语言模型在自动驾驶中推理响应性的框架，通过受控上下文变化比较基线和增强模型决策，验证人类原因对决策的影响。
摘要：
基础模型，包括视觉语言模型，越来越多地被用于自动驾驶，以解释场景、推荐行动并生成自然语言解释。然而，现有的评估方法主要评估结果性能，如安全性和轨迹准确性，而没有确定模型决策是否反映了人类相关考虑。因此，仍然不清楚由此类模型生成的解释是否对应于真正的推理响应决策，还是仅仅是事后合理化。这一限制在安全关键领域尤其显著，因为它可能产生虚假的信心。为了解决这一差距，我们提出了CARE Drive，即Context Aware Reasons Evaluation for Driving，一种用于评估视觉语言模型在自动驾驶中推理响应性的模型无关框架。CARE Drive在受控的上下文变化下比较基线和原因增强的模型决策，以评估人类原因是否因果地影响决策行为。该框架采用两阶段评估过程。提示校准确保稳定的输出。系统性的上下文扰动然后测量决策对人类原因如安全边际、社会压力和效率约束的敏感性。我们在涉及竞争性规范考虑的自行车超车场景中展示了CARE Drive。结果表明，显式的人类原因显著影响模型决策，提高了与专家推荐行为的一致性。然而，响应性在不同上下文因素中有所不同，表明对不同类型原因的敏感性不均。这些发现提供了实证证据，表明在不修改模型参数的情况下，可以系统地评估基础模型中的推理响应性。
英文摘要：
Foundation models, including vision language models, are increasingly used in automated driving to interpret scenes, recommend actions, and generate natural language explanations. However, existing evaluation methods primarily assess outcome based performance, such as safety and trajectory accuracy, without determining whether model decisions reflect human relevant considerations. As a result, it remains unclear whether explanations produced by such models correspond to genuine reason responsive decision making or merely post hoc rationalizations. This limitation is especially significant in safety critical domains because it can create false confidence. To address this gap, we propose CARE Drive, Context Aware Reasons Evaluation for Driving, a model agnostic framework for evaluating reason responsiveness in vision language models applied to automated driving. CARE Drive compares baseline and reason augmented model decisions under controlled contextual variation to assess whether human reasons causally influence decision behavior. The framework employs a two stage evaluation process. Prompt calibration ensures stable outputs. Systematic contextual perturbation then measures decision sensitivity to human reasons such as safety margins, social pressure, and efficiency constraints. We demonstrate CARE Drive in a cyclist overtaking scenario involving competing normative considerations. Results show that explicit human reasons significantly influence model decisions, improving alignment with expert recommended behavior. However, responsiveness varies across contextual factors, indicating uneven sensitivity to different types of reasons. These findings provide empirical evidence that reason responsiveness in foundation models can be systematically evaluated without modifying model parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
