Bayesian Persuasive Driving
For autonomous driving, this work addresses vehicle interaction by introducing a persuasion framework, but results are preliminary and incremental.
The paper proposes a Bayesian persuasive driving algorithm for autonomous vehicles to influence surrounding vehicles' beliefs and achieve lower costs for both, demonstrated in simulations.
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.