Exploring the safety impacts of conditionally automated driving vehicles switching control to manual driving mode within mixed traffic

Conditionally automated driving vehicles (CADVs) have been deployed in real-world testing and operational scenarios. However, research into their safety impacts on mixed traffic flow dynamics remains inadequate. Critical gaps persist in identifying key risk factors and understanding the mechanistic effects of various predictors. To address these research gaps, this study investigates the safety implications of CADVs during the transition to manual driving mode in mixed traffic environments. Vehicle trajectory data were collected through driving simulator experiments and used to calibrate parameters of a car-following model. Microscopic simulation tests were then designed and conducted, incorporating factors such as time budget, CADV penetration rate, takeover duration, and traffic volume. Based on the collected trajectories, time to collision was employed to evaluate rear-end conflict risks between the ego vehicle and the leading vehicle. An explainable machine learning model was developed to predict and interpret the impact of various factors on rear-end conflicts resulting from control transitions in CADVs. The model identified the five most influential factors affecting the ego vehicle’s rear-end conflict risk: acceleration and type of the leading vehicle, speed and following distance of the ego vehicle, and the speed difference between the two. The risk of rear-end collision was found to be negatively correlated with the ego vehicle’s speed, following distance, and the leading vehicle’s acceleration, whereas a greater speed difference increased conflict risk. Furthermore, control transitions in CADVs not only compromise their own safety but also increase the rear-end collision risk for following vehicles. These findings clarify the key factors influencing rear-end conflict risks during control transitions and highlight the broader safety implications of CADVs in mixed traffic, providing a basis for enhancing control strategies in such scenarios.