Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functions

TL;DR

This paper presents a control method for connected automated vehicles that guarantees safety and feasibility during merging by transforming the control problem into quadratic programs with added feasibility constraints, ensuring reliable safety guarantees.

Contribution

The paper introduces a novel feasibility constraint for control barrier functions, ensuring quadratic programs remain feasible and safety is guaranteed in merging control of CAVs.

Findings

The proposed method guarantees safety constraints are always feasible.

Simulations demonstrate improved safety and efficiency in vehicle merging.

The approach effectively balances travel time, energy consumption, and safety.

Abstract

We consider the merging control problem for Connected and Automated Vehicles (CAVs) aiming to jointly minimize travel time and energy consumption while providing speed-dependent safety guarantees and satisfying velocity and acceleration constraints. Applying the joint optimal control and control barrier function (OCBF) method, a controller that optimally tracks the unconstrained optimal control solution while guaranteeing the satisfaction of all constraints is efficiently obtained by transforming the optimal tracking problem into a sequence of quadratic programs (QPs). However, these QPs can become infeasible, especially under tight control bounds, thus failing to guarantee safety constraints. We solve this problem by deriving a control-dependent feasibility constraint corresponding to each CBF constraint which is added to each QP and we show that each such modified QP is guaranteed to be feasible. Extensive simulations of the merging control problem illustrate the effectiveness of this feasibility guaranteed controller.