There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. Our main contribution is the development of a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation-dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.

翻译：目前尚无成熟的方法用于评估自然交通冲突场景中人类响应时机的差异。源自受控实验的传统概念（如感知-响应时间）未能考虑人类响应的情境依赖性，且在许多常见交通冲突情境中无法明确界定刺激源。因此，这些概念在现实场景应用中存在局限。本研究的主要贡献在于开发了一个新颖的框架，用于测量和建模自然交通冲突中的响应时间，该框架适用于自动驾驶系统及其他交通安全领域。该框架提出，响应时机必须与主体当前的（先验）信念相关联，且始终嵌入并依赖于动态演变的情境。响应过程被建模为一种信念更新过程，该过程由主体感知到的对先验信念的违背（即意外刺激）所驱动。该框架解决了传统响应时间概念在自然场景应用中存在的两个关键局限：（1）响应时机的强情境依赖性；（2）如何明确界定刺激源。解决这些问题是为任何旨在应用于自然交通冲突的响应时机模型所必须面对的挑战。我们展示了如何通过一个相对简单的启发式模型实现该框架，该模型拟合了来自SHRP2数据集真实碰撞与近碰撞事件中的自然人响应数据，并讨论了该框架在原则上如何推广至任何交通冲突场景。我们还探讨了如何基于证据累积机制，结合基于机器学习的生成模型与信息论中的惊奇概念，实现该响应时机框架的计算化。