Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) require a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time-consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs using simulation platforms. Modeling the behavior of drivers or human operators in CAVs or other vehicles interacting with them is one of the main challenges of such simulations. While developing a perfect model for human behavior is a challenging task and an open problem, we present a significant augmentation of the current models used in simulators for driver behavior. In this paper, we present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles. In addition, we decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario, allowing for a thorough investigation of automated and active safety systems. Such representation through Interconnected modules offers a human-interpretable system that can be tuned to represent different classes of drivers. Additionally, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of various human-specific and system-specific factors, studying their effect on traffic network performance and safety.

翻译：开发联网与自动化车辆的安全和效率应用需要大量测试与评估。由于这些系统必须在关键和危险场景下运行，其评估工作成本高昂、可能存在危险且耗时漫长。作为替代方案，研究人员尝试利用仿真平台对算法和设计进行研究评估。在联网与自动化车辆或与之交互的其他车辆中，建模驾驶员或人类操作者的行为是此类仿真的主要挑战之一。尽管建立完美的人类行为模型极具挑战且尚未解决，我们提出了对仿真器中当前驾驶员行为模型的显著增强。本文展示了一个包含人类驾驶车辆与自动化车辆的混合交通系统仿真平台。此外，我们分解了人类驾驶任务，并提出了一种模块化方法来模拟大规模交通场景，从而实现对自动化系统与主动安全系统的深入研究。这种通过互连模块实现的表征方式构建了一个可解释的人类系统，能够调整以表示不同类别的驾驶者。同时，我们分析了一个大型驾驶数据集，提取了最能描述不同驾驶特征的表达性参数。最终，我们在仿真器中重建了密度相近的交通场景，对各类人类特定因素与系统特定因素进行了深入分析，研究其对交通网络性能与安全性的影响。