CHS: Small: Modeling Cyber Transportation and Human Interaction in Connected and Autonomous Vehicles

人的因素将构成未来网络运输系统(CTS)的关键组成部分，无论是作为联网车辆环境中的司机与无线消息交互，还是作为车辆自动化时的司机-乘客。鉴于任何工程系统都不会一直完美地工作，这就提出了与CTS和人类驾驶员之间交互作用的性质有关的重要问题。尽管当前文献的重点主要集中在硬件和软件上，但在这个开创性的项目中，Pi和他的研究团队将探索人与CTS的交互作为核心设计考虑因素。根据这一愿景，他们将开发和实验验证CTS的创新计算框架(数学模型)，该框架量化了系统设计参数对人类认知和性能关键要素的影响，从而能够推导出联网和自动车辆的设计要求和最佳参数设置，以最大限度地提高人的安全。对于联网车辆，该模型可用于确定最低通知要求(例如，消息传输可靠性)，以保持驾驶员的警觉性和最大限度地减少驾驶员的分心。对于自动车辆，该框架可用于得出可接受的最低系统可靠性、最优重复练习以最大限度地减少运动技能衰退、以及通知驾驶员接管车辆的消息的最佳提前期和设计(机器对人工驾驶员的移交)。项目成果将通过预测技术对驾驶员行为的影响，为该领域的研究人员提供第一个工具，以了解人-CTS交互的基本机制(例如，系统可靠性对人的接受和信任的影响)。该软件将是开源的，因此CTS的研究人员可以很容易地修改他们的设计和/或探索修改现有功能和/或添加新功能的效果，因为技术的变化可能会得到保证。

The human element will constitute a critical component of tomorrow's Cyber Transportation Systems (CTS), either as drivers interacting with wireless messages in connected vehicle settings or as driver-passengers when vehicles are automated. Given that no engineered system will work perfectly all of the time, this raises important questions relating to the nature of the interaction between CTS and the human driver. Whereas the focus of the current literature has primarily been on hardware and software, in this pioneering project the PI and his research team will instead explore human-CTS interaction as the central design consideration. In accordance with this vision, they will develop and experimentally validate an innovative computational framework (mathematical model) for CTS that quantifies the effects of system design parameters on the key elements of human cognition and performance, which in turn will enable the derivation of design requirements and optimal parameter settings for connected and automated vehicles in order to maximize human safety.For connected vehicles the model could be employed to determine minimal notification requirements (e.g., message transmission reliability) so as to maintain driver alertness and minimize driver distractions. For automated vehicles, the framework could be utilized to derive the minimal acceptable system reliability, optimal re-practice to minimize motor skill decay, and optimal lead time and design of the messages informing a driver to take over control of the vehicle (machine to human driver hand-over). Project outcomes will provide researchers in the field with the first tool to understand the fundamental mechanisms of human-CTS interaction (e.g., the impact of system reliability on human acceptance and trust), by predicting the effects of technology on driver behavior. The software will be open source, so that CTS researchers can easily modify their designs and/or explore the effect of modifying existing features and/or adding new ones as changes in technology may warrant.