This study examines the impact of conditionally automated driving vehicles (CADVs) handing control over to human drivers on the string stability of mixed traffic. It specifically considers various penetration rates of CADVs and different time-to-collision (TTC) thresholds set at 5, 7, and 9 s. To gather relevant vehicle trajectory data, driving simulator tests were performed, capturing both the normal car-following and the post-takeover processes. Then, the intelligent driver model parameters were calibrated for these two processes, respectively. Additionally, a car-following model was developed to incorporate the dynamic takeover process of CADVs within the heterogeneous traffic. Theoretical analysis was conducted to determine the string stability boundary of heterogeneous traffic, with numerical simulation tests performed to validate the results. The results indicate that the transition of control from CADVs to human drivers significantly contributed to the instability of mixed traffic. Specifically, a higher TTC correlated with more CADVs transferring control to human drivers. The increase in the takeover rate from CADVs to human drivers resulted in pronounced instability within heterogeneous traffic flows. Additionally, the simultaneous control transition of multiple CADVs was associated with reduced minimum platoon speed, exacerbating traffic congestion. Conversely, enhancing the permeability of CADVs led to smoother traffic conditions and suppressed the formation of unstable traffic waves. The findings underscore the significant impact that the transition of control from CADVs to human drivers has on the stability of heterogeneous traffic. This lays a crucial foundation for implementing cooperative safety control measures within such traffic systems. Moreover, implementing a 5-second takeover request lead time in automated driving systems can effectively reduce the takeover rate of CADVs, thereby enhancing overall traffic stability.
Exploring the Impact of Conditionally Automated Driving Vehicles Transferring Control to Human Drivers on the Stability of Heterogeneous Traffic Flow
IEEE Transactions on Intelligent Vehicles ; 10 , 2 ; 912-928
01.02.2025
4875129 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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