Future-Focused Control Barrier Functions for Autonomous Vehicle Control

TL;DR

This paper introduces future-focused control barrier functions (ff-CBF) to enhance autonomous vehicle control at intersections, demonstrating improved safety and efficiency in simulations and real-world experiments.

Contribution

The paper proposes a novel ff-CBF framework that predicts collision avoidance over long horizons, including a relaxed version (rff-CBF) for better performance.

Findings

rff-CBF outperforms benchmark controllers in simulations

Improved intersection throughput while maintaining safety

Successful real-world demonstration with ground rovers

Abstract

In this paper, we introduce a class of future-focused control barrier functions (ff-CBF) aimed at improving traditionally myopic CBF based control design and study their efficacy in the context of an unsignaled four-way intersection crossing problem for collections of both communicating and non-communicating autonomous vehicles. Our novel ff-CBF encodes that vehicles take control actions that avoid collisions predicted under a zero-acceleration policy over an arbitrarily long future time interval. In this sense the ff-CBF defines a virtual barrier, a loosening of which we propose in the form of a relaxed future-focused CBF (rff-CBF) that allows a relaxation of the virtual ff-CBF barrier far from the physical barrier between vehicles. We study the performance of ff-CBF and rff-CBF based controllers on communicating vehicles via a series of simulated trials of the intersection scenario, and in particular highlight how the rff-CBF based controller empirically outperforms a benchmark controller from the literature by improving intersection throughput while preserving safety and feasibility. Finally, we demonstrate our proposed ff-CBF control law on an intersection scenario in the laboratory environment with a collection of 5 non-communicating AION ground rovers.