Bayesian Persuasive Driving

TL;DR

This paper introduces a Bayesian persuasive driving algorithm enabling autonomous vehicles to influence surrounding vehicles' beliefs through signaling, improving interaction safety and efficiency in traffic scenarios.

Contribution

It proposes a novel Bayesian persuasion framework for autonomous driving that models vehicle interactions as belief updates via signaling, enhancing decision-making.

Findings

Effective in diverse traffic scenarios

Handles vehicles with different driving behaviors

Reduces interaction costs

Abstract

In the autonomous driving area, interaction between vehicles is still a piece of puzzle which has not been fully resolved. The ability to intelligently and safely interact with other vehicles can not only improve self driving quality but also be beneficial to the global driving environment. In this paper, a Bayesian persuasive driving algorithm based on optimization is proposed, where the ego vehicle is the persuader (information sender) and the surrounding vehicle is the persuadee (information receiver). In the persuasion process, the ego vehicle aims at changing the surrounding vehicle's posterior belief of the world state by providing certain information via signaling in order to achieve a lower cost for both players. The information received by the surrounding vehicle and its belief of the world state are described by Gaussian distributions. Simulation results in several common traffic scenarios are provided to demonstrate the proposed algorithm's capability of handling interaction situations involving surrounding vehicles with different driving characteristics.