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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Implementation of a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments
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Implementation of a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments

机译：基于势场的自主车辆在复杂环境中驾驶的决策算法的实现

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Autonomous driving is undergoing huge developments nowadays. It is expected that its implementation will bring many benefits. Autonomous cars must deal with tasks at different levels. Although some of them are currently solved, and perception systems provide quite an accurate and complete description of the environment, high-level decisions are hard to obtain in challenging scenarios. Moreover, they must comply with safety, reliability and predictability requirements, road user acceptance, and comfort specifications. This paper presents a path planning algorithm based on potential fields. Potential models are adjusted so that their behavior is appropriate to the environment and the dynamics of the vehicle and they can face almost any unexpected scenarios. The response of the system considers the road characteristics (e.g., maximum speed, lane line curvature, etc.) and the presence of obstacles and other users. The algorithm has been tested on an automated vehicle equipped with a GPS receiver, an inertial measurement unit and a computer vision system in real environments with satisfactory results.
机译：如今，自动驾驶正经历着巨大的发展。预计其实施将带来许多好处。自动驾驶汽车必须处理不同级别的任务。尽管其中一些问题目前已解决，并且感知系统提供了对环境的准确而完整的描述，但是在具有挑战性的场景中很难获得高层决策。此外，它们必须符合安全性，可靠性和可预测性要求，道路使用者的接受程度和舒适性规范。本文提出了一种基于势场的路径规划算法。调整了潜在模型，使其行为适合于环境和车辆动力学，并且它们几乎可以面对任何意外情况。系统的响应会考虑道路特征（例如，最大速度，车道线曲率等）以及障碍物和其他用户的存在。该算法已经在配备有GPS接收器，惯性测量单元和计算机视觉系统的自动车辆上在真实环境中进行了测试，结果令人满意。

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