School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, Beihang University, Beijing 100191, China.
School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, Beihang University, Beijing 100191, China.
Accid Anal Prev. 2024 May;199:107530. doi: 10.1016/j.aap.2024.107530. Epub 2024 Mar 3.
Merging areas serve as the potential bottlenecks for continuous traffic flow on freeways. Traffic incidents in freeway merging areas are closely related to decision-making errors of human drivers, for which the autonomous vehicles (AVs) technologies are expected to help enhance the safety performance. However, evaluating the safety impact of AVs is challenging in practice due to the lack of real-world driving and incident data. Despite the increasing number of simulation-based AV studies, most relied on single traffic/vehicle driving simulators, which exhibit limitations such as inaccurate description of AV behavior using pre-defined driving models, limited testing modules, and a lack of high-fidelity traffic scenarios. To this end, this study addresses these challenges by customizing different types of car-following models for AVs on freeway and developing a software-in-the-loop co-simulation platform for safety performance evaluation. Specifically, the environmental perception module is integrated in PreScan, the decision-making and control model for AVs is designed by Matlab, and the traffic flow environment is established by Vissim. Such a co-simulation platform is supposed to be able to reproduce the mixed traffic with AVs to a large extent. By taking a real freeway merging scenario as an example, comprehensive experiments were conducted by introducing a single AV and multiple AVs on the mainline of freeway, respectively. The single AV experiment investigated the performance of different car-following models microscopically in the case of merging conflict. The safety and comfort of AVs were examined in terms of TTC and jerk, respectively. The multiple AVs experiment examined the safety impact of AVs on mixed traffic of freeway merging areas macroscopically using the developed risk assessment model. The results show that AVs could bring significant benefits to freeway safety, as traffic conflicts and risks are substantially reduced with incremental market penetration rates.
合流区是高速公路连续交通流的潜在瓶颈。高速公路合流区的交通事件与驾驶员的决策错误密切相关，预计自动驾驶汽车 (AV) 技术将有助于提高安全性。然而，由于缺乏实际驾驶和事故数据，评估 AV 的安全影响在实践中具有挑战性。尽管基于仿真的 AV 研究越来越多，但大多数研究都依赖于单一的交通/车辆驾驶模拟器，这些模拟器存在使用预定义驾驶模型不准确描述 AV 行为、有限的测试模块以及缺乏高保真交通场景等局限性。为此，本研究通过为高速公路上的 AV 定制不同类型的跟驰模型，并开发用于安全性能评估的软件在环协同仿真平台来解决这些挑战。具体来说，环境感知模块集成在 PreScan 中，AV 的决策和控制模型由 Matlab 设计，交通流环境由 Vissim 建立。这样的协同仿真平台应该能够在很大程度上再现具有 AV 的混合交通。以真实的高速公路合流场景为例，通过在高速公路主线分别引入一辆 AV 和多辆 AV 进行了全面的实验。单 AV 实验在合流冲突的情况下微观研究了不同跟驰模型的性能。分别从 TTC 和急动度两个方面考察了 AV 的安全性和舒适性。多 AV 实验使用所开发的风险评估模型宏观考察了 AV 对高速公路合流区混合交通的安全影响。结果表明，随着市场渗透率的增加，交通冲突和风险大大降低，AV 可为高速公路安全带来显著效益。
