Personalized Car-Following Shared Control With Group-Oriented Traffic Smoothing Properties

Evidences have been provided that the effect of poorly designed vehicle automation systems may propagate from the single car up to the traffic dynamics. A reported example consists of car-following driver assistance systems triggering destabilizing group phenomena like phantom traffic jams and stop-and-go waves. It then becomes fundamental to study ‘group-oriented’ vehicle shared control algorithms that are able to assist the driver while at the same time prevent the propagation of destabilizing effects in the traffic when these systems are widely deployed. A key challenge in vehicle shared control is that the heterogeneity and uncertainty of human driving characteristics require personalized adaptation: it is an open problem to realize stabilizing traffic properties from personalized adaptive vehicle shared control. The distinguishing contribution of this work is a car-following shared control method that, while adapting to the personal characteristics of each driver, contains a ‘group’ model with desirable traffic properties defined in terms of string stability and collision avoidance. It is proven analytically that the proposed shared control is able to assist each driver in approaching the group model adaptively (i.e., handling heterogeneity and uncertainties) and optimally (i.e., with minimum control authority over the driver). Numerical experiments performed in SUMO with Highway Fuel Economy Test Cycle (HWFET) data and stop-and-go wave data validate that the proposed assistance improves traffic smoothing while handling heterogeneity and uncertainties in the driver parameters.