Car-Following Behavior Preferences and Influencing Factors on Long Steep Downhill Sections Under Active Prevention and Control Strategies

To mitigate driving risks from brake failure on long and steep downhill sections, this study designs three deployment schemes for radar–video fusion devices: a baseline scenario with no coverage, a scenario with partial coverage in high-risk areas, and a scenario with full coverage. Corresponding information service strategies are delivered via Human–Machine Interfaces (HMIs), forming an integrated active prevention and control framework from risk perception to preventive action. Driving simulation experiments focusing on the car-following process were conducted to collect vehicle operational data and extract characteristic indicators based on the Wiedemann model. A Generalized Linear Mixed Model was employed to comprehensively examine the effects of HMIs on car-following behavior to identify the optimal active prevention strategy. Results show that drivers exhibit greater caution under the partial coverage scheme, with time headway increasing by 47.63% compared to the scheme with no radar–video fusion devices to ensure safety. Under full coverage conditions, drivers can obtain real-time information about the leading vehicle’s status and the distance between the two vehicles in key risk sections. Drivers choose to follow the leading vehicle, balancing both safety in car-following and efficiency on long and steep downhill sections. As the level of accompanying services improves, drivers engage in self-regulation to avoid rear-end collisions. Particularly under the scheme with full coverage of radar–video fusion devices, the standing distance significantly increases by 219.37% compared to the partial coverage condition. Drivers demonstrate optimal vehicle control capabilities. Furthermore, there is an interaction effect between the accompanying service strategy and drivers’ attributes on car-following behaviors. Under different schemes, more experienced drivers exhibit a certain degree of aggressiveness, providing a basis for the targeted design of information services for different types of drivers. The findings support the deployment and application of risk perception and prevention devices on long and steep downhill sections, which can effectively enhance the comprehensive safety of such special roads in the connected vehicle environment.