Socially Compliant Control of Autonomous Vehicles with Application to Eco-Driving

TL;DR

This paper introduces a framework for socially compliant autonomous vehicle control that considers human driver behavior, improving safety and cooperation through utility-based optimization and eco-driving applications.

Contribution

It develops a novel control framework incorporating social value orientation to enable AVs to behave socially responsibly towards human drivers.

Findings

The framework effectively models social interactions between AVs and HVs.

Numerical results demonstrate improved eco-driving performance and social compliance.

Real-world data validates the approach's practical applicability.

Abstract

Control design of autonomous vehicles (AVs) has mostly focused on achieving a prespecified goal for an individually controlled AV or for a swarm of cooperatively controlled AVs. However, the impact of autonomous driving on human-driven vehicles (HVs) has been largely ignored in AV controller synthesis, which could result in egoistic AV behavior detrimental to the safety of passengers and surrounding traffic. In this study we develop a general framework for socially compliant control design of AVs with a useful metric of social psychology, called social value orientation (SVO), allowing AVs to leverage their impact on the behavior of the following HVs. This is critical since AVs that behave in a socially compliant manner enable human drivers to comprehend their actions and respond appropriately. Within the proposed framework, we define the utilities of the controlled AV and its following vehicle, to be maximized in a weighted fashion determined by the AV's SVO. The utility maximization covers an array of design objectives given the goal of the AV and the benefits for the following HV stemming from the courtesy of socially compliant AV controls. An optimal control problem is then formulated to maximize the utility function defined, which is numerically solved using Pontryagin's minimum principle with optimality guarantees. The methodology developed is applied to synthesize socially compliant control for eco-driving of AVs. A set of numerical results are presented to show the mechanism and effectiveness of the proposed approach using real-world experimental data collected on Highway 55 in Minnesota.