On the robotic uncertainty of fully autonomous traffic: From stochastic car-following to mobility–safety trade-offs
Published in TRANSPORTATION RESEARCH PART C:Emerging Technologies, 2025
This paper presents an analytical model framework that delineates the endogenous reciprocity between traffic safety and mobility that arises from AVs’ robotic uncertainties. Using both realistic car-following data and a stochastic intelligent driving model (IDM), the stochastic car-following distance is derived as a key parameter, enabling analysis of single-lane capacity and collision probability. A semiMarkov process is then employed to model the dynamics of the lane capacity, and the resulting collision-inclusive capacity, representing expected lane capacity under stationary conditions, serves as the primary performance metric for fully autonomous traffic. The analytical results are further utilized to investigate the impacts of critical parameters in AV and roadway designs on traffic performance, as well as the properties of optimal speed and headway under mobilitytargeted or safety-dominated management objectives.
Recommended citation: Li, H., Sun, X., Zhuang, C., & Li, X. (2025). On the robotic uncertainty of fully autonomous traffic: From stochastic car-following to mobility–safety trade-offs. Transportation Research Part C: Emerging Technologies, 178, 105254.
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