Yang Zi, Yao Yaojie, Zhang Liyan
School of Automation, Nanjing University of Science and Technology, Nanjing 210044, China.
School of Intelligent Manufacturing, Nanjing University of Science and Technology Zijin College, Nanjing 210023, China.
Sensors (Basel). 2025 Jun 4;25(11):3544. doi: 10.3390/s25113544.
This study optimizes the deployment of autonomous taxis for safety at skewed intersections through a simulation-based approach, identifying an optimal penetration rate and control strategies. Here, we investigate the safety impacts of autonomous taxis (ATs) at such intersections using a simulation-based approach, leveraging the VISSIM traffic simulation tool and the Surrogate Safety Assessment Model (SSAM). Our study identifies an optimal AT penetration rate of approximately 0.5-0.7, as exceeding this range may lead to a decline in safety metrics such as TTC and PET. We find that connectivity among ATs does not linearly correlate with safety improvements, suggesting a nuanced approach to AT deployment is necessary. The "Normal" control strategy, which mimics human driving, shows a direct proportionality between AT penetration and TTC, indicating that not all levels of automation enhance safety. Our conflict analysis reveals distinct patterns for crossing, lane-change, and rear-end conflicts under various control strategies, underscoring the need for tailored approaches at skewed intersections. This research contributes to the discourse on AT safety and informs the development of traffic management strategies and policy frameworks that prioritize safety and efficiency in the context of skewed intersections.
本研究通过基于模拟的方法优化了在斜交路口自动驾驶出租车的部署以提高安全性，确定了最佳渗透率和控制策略。在此，我们利用VISSIM交通模拟工具和替代安全评估模型（SSAM），通过基于模拟的方法研究了自动驾驶出租车（AT）在这类路口的安全影响。我们的研究确定了约0.5-0.7的最佳AT渗透率，因为超过这个范围可能会导致诸如TTC和PET等安全指标下降。我们发现AT之间的连通性与安全改善并非线性相关，这表明需要采用细致入微的方法来部署AT。模仿人类驾驶的“正常”控制策略显示，AT渗透率与TTC之间存在直接比例关系，这表明并非所有自动化程度都能提高安全性。我们的冲突分析揭示了在各种控制策略下交叉、变道和追尾冲突的不同模式，强调了在斜交路口需要采用量身定制的方法。本研究为关于AT安全的讨论做出了贡献，并为在斜交路口背景下优先考虑安全和效率的交通管理策略和政策框架的制定提供了参考。
