Self-Delayed Feedback Based Car Following Control With Velocity Uncertainty of Preceding Vehicle On Gradient Roads

Uphill and downhill roads are prevalent in mountainous areas and freeways. Despite the advancement of vehicle-to-vehicle (V2V) communication technology, the driving field of vision is still largely limited under such a complex road environment, which hinders the sensors accurately perceiving the speed of the front vehicle. As such, a fundamental question for autonomous traffic management is how to control traffic flow associated with the velocity uncertainty of preceding vehicles? This paper seeks to develop a controlling framework for corporative car following control under such complex road environment. To this end, we first propose a traffic flow model accounting for the uncertainty effect of preceding vehicles velocity on gradient road. We further design a new self-delayed feedback controller based on the velocity and headway difference between the current time step and historical time step, in an aim to enhance the robustness of traffic flow. The sufficient condition where traffic jam does not occur is derived from the perspective of the frequency domain via Hurwitz criteria H∞ and norm of transfer functions. The bode diagram reveals that the robustness of closed-loop traffic flow model has been significantly enhanced. We also conduct simulations to verify the theoretical analysis.