SESF State-Oriented Vehicle-Following Control Based on Real-Time Tracking of Dynamic Safety Intervehicle Distance

The ability of a vehicle-following system to establish and maintain a safe and efficient steady-following (SESF) state, recover a previous SESF state, and create a new SESF state is crucial for assessing and demonstrating the levels of automation and intelligence in vehicle-following systems and unmanned autonomous vehicles. To endow the vehicle-following system with this ability, the SESF state is defined as the target for controlling and optimizing vehicle-following behavior. Building on this definition, we explore the control mechanisms governing the establishment and maintenance of a SESF state, the recovery of a previous SESF state, and the recreation of a new SESF state within the vehicle-following system. Guided by these mechanisms, we construct an SESF state-oriented model predictive control (MPC) model, leveraging real-time tracking of dynamic safety intervehicle distance. Subsequently, a hierarchical algorithm is designed, incorporating a SESF state-oriented global process optimization alongside safety and comfort constraints. This approach aims to enhance the performance of vehicle-following control toward SESF state. Simulation results confirm that when the preceding vehicle maintains a constant velocity for a sufficient duration, the following vehicle, employing the proposed control method, adjusts its behavior to achieve the SESF state while ensuring safety, efficiency, and smoothness. The validation results underscore the viability and effectiveness of the SESF state-oriented vehicle-following control method in establishing, maintaining, recovering, and creating the SESF state. These findings provide nuanced perspectives for optimizing and controlling vehicle-following behavior.