An Optimal Control Framework for Influencing Human Driving Behavior in Mixed-Autonomy Traffic

TL;DR

This paper introduces an optimal control framework using control barrier functions to influence human driving behavior in mixed-autonomy traffic, aiming to improve safety and traffic flow by proactively guiding human drivers.

Contribution

It presents a novel, versatile control framework that can influence human drivers' behaviors in various traffic scenarios, including multi-robot and multi-human systems.

Findings

Framework effectively influences human driving behavior in simulations

Demonstrates improved traffic flow and safety in case studies

Versatile across different influence objectives and configurations

Abstract

As autonomous vehicles (AVs) become increasingly prevalent, their interaction with human drivers presents a critical challenge. Current AVs lack social awareness, causing behavior that is often awkward or unsafe. To combat this, social AVs, which are proactive rather than reactive in their behavior, have been explored in recent years. With knowledge of robot-human interaction dynamics, a social AV can influence a human driver to exhibit desired behaviors by strategically altering its own behaviors. In this paper, we present a novel framework for achieving human influence. The foundation of our framework lies in an innovative use of control barrier functions to formulate the desired objectives of influence as constraints in an optimal control problem. The computed controls gradually push the system state toward satisfaction of the objectives, e.g. slowing the human down to some desired speed. We demonstrate the proposed framework's feasibility in a variety of scenarios related to car-following and lane changes, including multi-robot and multi-human configurations. In two case studies, we validate the framework's effectiveness when applied to the problems of traffic flow optimization and aggressive behavior mitigation. Given these results, the main contribution of our framework is its versatility in a wide spectrum of influence objectives and mixed-autonomy configurations.