Abstract

Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under different market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections.

摘要

联网自动驾驶汽车（CAV）可以通过减少人为错误来潜在地改善运输系统的性能。本文研究了交叉路口与传统车辆混合行驶时CAV的安全影响。分析加利福尼亚的真实世界的AV碰撞显示，交叉路口的后端碰撞是主要的碰撞类型。因此，为了加深我们对交叉路口CAV未来人类驱动车辆之间相互作用的理解，开发了一个仿真框架来对自动驾驶汽车（AV），协作式AV和常规人类驱动汽车的混合交通环境进行建模。为了对AV的驾驶行为建模，利用了自适应巡航控制（ACC）和协作式ACC（CACC）模型。尤其，这项研究探索了由于在不同的CAV市场渗透情况下实现自动化和连通性而导致的系统改进。ACC和CACC汽车跟随模型用于模仿AV和协作AV的行为。现实世界中连接的车辆数据用于修改和调整Wiedemann模型的加速/减速方式。接下来，利用捕获了车速变化的驾驶波动性概念来校准模拟，以表示真实环境的安全性能。在不同的CAV市场渗透率下，有两种替代安全措施用于评估代表性路口的安全性能：纵向冲突的数量和行驶波动。在ACC市场渗透率较低的情况下，发现安全性改善很小，但是安全性有了显着提高，ACC渗透率超过40％。通过CACC增加合作和连接性，可以更快地实现其他安全改进。此外，发现ACC / CACC车辆可改善交叉路口的平均速度和行驶时间。