贝叶斯说服驾驶
Bayesian Persuasive Driving
AI总结：
本文提出了一种基于优化的贝叶斯说服驾驶算法，通过 ego 车辆向周围车辆发送信息以改变其对世界状态的后验信念，从而在降低双方成本的同时提高自动驾驶质量和整体驾驶环境。
摘要：
在自动驾驶领域，车辆间的交互仍是一个未完全解决的谜题。智能且安全地与其他车辆交互不仅能提高自动驾驶质量，也对整体驾驶环境有益。本文提出了一种基于优化的贝叶斯说服驾驶算法，其中 ego 车辆是说服者（信息发送者），周围车辆是被说服者（信息接收者）。在说服过程中，ego 车辆通过信号传递提供某些信息，以改变周围车辆对世界状态的后验信念，从而实现双方的低成本交互。周围车辆接收到的信息及其对世界状态的信念由高斯分布描述。在几种常见的交通场景中提供了仿真结果，以展示所提出算法在处理具有不同驾驶特征的周围车辆交互情况时的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
