CAREER: Data-driven Multiscale Modeling of Complex Traffic Systems Utilizing Networked Driving Simulators

这个教师早期职业发展（CAREER）奖支持研究，将调查人类驾驶行为和交通参与者之间的互动，旨在推动自动驾驶和联网车辆的技术进步。了解人类驾驶行为对于设计与人类安全共享道路的互联和自动驾驶汽车至关重要。该奖项支持有关人类驾驶行为的基础研究，包括他们与其他驾驶员和环境的生理和认知参与。为了确保安全和具有成本效益的方法，该项目将使用沉浸式虚拟现实驾驶模拟器。控制和可重复的实验将在这种环境中进行，系统地暴露司机的各种交通情况。该项目将整合教育活动，向学生，包括来自代表性不足群体的学生，介绍STEM主题，以及宣传活动，以提高公众对交通安全，二次碰撞和驾驶障碍的认识。这个CAREER项目将研究多尺度交通相互作用，在车辆，驾驶员和认知水平。通过对多个互动参与者进行脑部扫描，该项目将研究在群体水平上驾驶员之间是否出现大脑耦合特征，以及认知水平的参与与其他驾驶行为之间的关系。研究结果对驾驶员辅助系统的认知和行为神经科学以及技术进步具有潜在的变革意义。该项目将生成丰富的多尺度数据集，确定实验知情的建模参数，并发现实验验证的交通模型。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This Faculty Early Career Development (CAREER) award supports research that will investigate human driving behavior and interactions among traffic participants, with the aim of empowering technological advances in autonomous and connected vehicles. Understanding human driving behavior is vital for engineering connected and autonomous vehicles that safely share roads with humans. This award supports fundamental research about human driving behavior, including their physiological and cognitive engagement with other drivers and the environment. To ensure a safe and cost-effective approach, the project will utilize an immersive virtual reality driving simulator. Controlled and repeatable experiments will be conducted in this environment by systematically exposing drivers to a variety of traffic scenarios. The project will integrate educational activities that introduce students, including students from underrepresented groups, to STEM topics, as well as outreach activities to raise awareness of the general public to traffic safety, secondary crashes, and impaired driving. This CAREER project will study multiscale traffic interactions, at the vehicle, driver, and cognitive levels. Using brain scans for multiple interacting participants, this project will investigate whether brain coupling characteristics emerge among drivers at the group level, and how cognitive level engagement relates to other driving behaviors. The research findings have potentially transformative implications for cognitive and behavioral neuroscience and technological advancement in driver assistance systems. The project will generate rich multiscale datasets, identify experimentally-informed modeling parameters, and discover experimentally-validated traffic models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.