Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change

TL;DR

This paper presents a rule-based control framework using control barrier functions and quadratic programming to ensure safe autonomous lane changes in complex traffic environments.

Contribution

It introduces a finite state machine combined with CLF-CBF-QP for safe, automatic lane change maneuvers in autonomous vehicles.

Findings

Controller guarantees safety at high update frequency.

Effective in both typical and random driving scenarios.

Enables collision-free lane changes.

Abstract

This paper develops a new control design for guaranteeing a vehicle's safety during lane change maneuvers in a complex traffic environment. The proposed method uses a finite state machine (FSM), where a quadratic program based optimization problem using control Lyapunov functions and control barrier functions (CLF-CBF-QP) is used to calculate the system's optimal inputs via rule-based control strategies. The FSM can make switches between different states automatically according to the command of driver and traffic environment, which makes the ego vehicle find a safe opportunity to do a collision-free lane change maneuver. By using a convex quadratic program, the controller can guarantee the system's safety at a high update frequency. A set of pre-designed typical lane change scenarios as well as randomly generated driving scenarios are simulated to show the performance of our controller.