Trust-Based and Individualizable Adaptive Cruise Control Using Control Barrier Function Approach With Prescribed Performance

Abstract

Trust is a crucial aspect for autonomous vehicles (AVs) and automated driving systems (ADSs) that maintains humans’ acceptance of such functions. However, trust dynamic models describing the human-vehicle relationship rarely exist. To this end, this paper develops a quantitative trust dynamic model of the driver to an adaptive cruise control (ACC) system and applies the proposed model to a trust-based ACC. Driver’s trust level is modeled as an objective evaluation index for assessing the individual perceived trustworthiness on automation. Three contributions have been made in this work: 1) a novel quantitative dynamic model describing the driver’s trust on ACC is proposed for the first time considering the effects of the driver’s taking-over and handing-over operations as well as the steady-state driving scenarios; 2) an improved control barrier function approach with guaranteed system stability and prescribed asymptotic performance is proposed to design the trusted-based ACC, satisfying the state, input, and safety constraints; and 3) a new prescribed performance function that does not require accurate value of the initial condition is proposed to restrict the tracking errors within the predefined asymptotic boundaries, given that the initial value of the trust is hard to obtain. High-fidelity CarSim-based simulations demonstrate the rationality of the proposed trust model and the validity of the proposed control approach.

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