Maximizing Road Capacity Using Cars that Influence People

TL;DR

This paper proposes a novel approach to increase road capacity by influencing human drivers through planned interactions with autonomous vehicles, optimizing vehicle arrangements for better traffic flow.

Contribution

It introduces a new algorithm that uses local interactions to proactively influence human driver behavior and maximize road capacity in mixed traffic scenarios.

Findings

Theoretical characterization of capacity increase through platooning.

Algorithm successfully reorders vehicles to improve traffic flow in simulations.

Demonstrates potential for autonomous vehicles to influence human drivers effectively.

Abstract

The emerging technology enabling autonomy in vehicles has led to a variety of new problems in transportation networks, such as planning and perception for autonomous vehicles. Other works consider social objectives such as decreasing fuel consumption and travel time by platooning. However, these strategies are limited by the actions of the surrounding human drivers. In this paper, we consider proactively achieving these social objectives by influencing human behavior through planned interactions. Our key insight is that we can use these social objectives to design local interactions that influence human behavior to achieve these goals. To this end, we characterize the increase in road capacity afforded by platooning, as well as the vehicle configuration that maximizes road capacity. We present a novel algorithm that uses a low-level control framework to leverage local interactions to optimally rearrange vehicles. We showcase our algorithm using a simulated road shared between autonomous and human-driven vehicles, in which we illustrate the reordering in action.