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.

中文翻译：

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基于联合仿真平台的自动驾驶环境下高速公路合流区安全性能评估

合并区域是高速公路上持续交通流的潜在瓶颈。高速公路合流区的交通事故与人类驾驶员的决策失误密切相关，自动驾驶汽车（AV）技术有望帮助提升安全性能。然而，由于缺乏现实世界的驾驶和事故数据，评估自动驾驶汽车的安全影响在实践中具有挑战性。尽管基于模拟的自动驾驶研究数量不断增加，但大多数都依赖于单一交通/车辆驾驶模拟器，这些模拟器存在局限性，例如使用预定义的驾驶模型对自动驾驶行为的描述不准确、测试模块有限以及缺乏高保真交通场景。为此，本研究通过为高速公路上的自动驾驶汽车定制不同类型的跟车模型并开发用于安全性能评估的软件在环联合仿真平台来应对这些挑战。具体来说，在PreScan中集成环境感知模块，利用Matlab设计自动驾驶汽车的决策和控制模型，利用Vissim建立交通流环境。这样的联合仿真平台应该能够在很大程度上重现与自动驾驶汽车的混合流量。以真实的高速公路合流场景为例，在高速公路主线上分别引入单台自动驾驶汽车和多台自动驾驶汽车进行综合实验。单路自动驾驶实验从微观角度研究了不同跟驰模型在并道冲突情况下的表现。AV 的安全性和舒适性分别通过 TTC 和加加速度进行检验。多自动驾驶汽车实验利用开发的风险评估模型，从宏观上检验了自动驾驶汽车对高速公路合流区混合交通的安全影响。结果表明，自动驾驶汽车可以为高速公路安全带来显着的好处，因为随着市场渗透率的提高，交通冲突和风险会大大减少。