  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.
