Simultaneous Lane-Keeping and Obstacle Avoidance by Combining Model Predictive Control and Control Barrier Functions

TL;DR

This paper presents a hierarchical control approach combining Model Predictive Control and Control Barrier Functions to achieve simultaneous lane-keeping and obstacle avoidance, ensuring safety and feasibility in various scenarios.

Contribution

It introduces a novel hierarchical control framework integrating MPC with CBF-based safety filters, including input-constrained variants, for improved vehicle safety and performance.

Findings

CBF safety filters effectively override MPC control when necessary.

Input-constrained CBFs maintain safety under input limitations.

Simulation results demonstrate improved lane-keeping and obstacle avoidance performance.

Abstract

In this work, we combine {Model Predictive Control} (MPC) and Control Barrier Function (CBF) design {methods} to create a hierarchical control law for simultaneous lane-keeping (LK) and obstacle avoidance (OA): at the low level, MPC performs LK via trajectory tracking during nominal operation; and at the high level, different CBF-based safety filters that ensure both LK and OA are designed and compared across some practical scenarios. In particular, we show that Exponential Safety (ESf) and Prescribed-Time Safety (PTSf) filters, which override the MPC control when necessary, result in feasible Quadratic Programs when safety is prioritized appropriately. We additionally investigate control designs subject to input constraints by using Input-Constrained-CBFs. Finally, we compare the performance of combinations of ESf, PTSf, and their input-constrained counterparts with respect to the LK and OA goals in two simulation studies for early- and late-detected obstacle scenarios.