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首页> 外文会议>International Conference on Security of Smart Cities, Industrial Control System and Communications >“My autonomous car is an elephant”: A Machine Learning based Detector for Implausible Dimension
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“My autonomous car is an elephant”: A Machine Learning based Detector for Implausible Dimension

机译：“我的自动驾驶汽车是一头大象”：基于机器学习的不可行维检测器

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摘要

Connected and Automated Vehicle is the next goal for car manufacturers towards traffic safety and efficiency. To ensure safety, automotive applications rely on data acquired through vehicular communication and locally embedded sensors. Among these data, classification data permit the autonomous vehicle to decide to pass another vehicle according to not only its dynamic but also its length and width. Unlike sensors which are prone to measurement errors, vehicular communication allows others connected vehicles to provide their exact dimension values based on car manufacturer specification. However, this fact assumes that other road users may not lie. Currently, researchers focus on malicious mobility data but none focus on classification data within V2X message. Therefore, this paper proposes a misbehavior classifier related to classification data for multiple types of road users. Thus, we compare four methods that include a threshold classifier (MinMax) and three machine learning algorithms.
机译：联网和自动驾驶汽车是汽车制造商实现交通安全和效率的下一个目标。为了确保安全，汽车应用依赖于通过车辆通讯和本地嵌入式传感器获取的数据。在这些数据中，分类数据允许自主车辆不仅根据其动态而且还根据其长度和宽度来决定通过另一辆车辆。与容易出现测量误差的传感器不同，车辆通信允许其他连接的车辆根据汽车制造商的规格提供其准确的尺寸值。但是，此事实假定其他道路使用者可能不会撒谎。当前，研究人员专注于恶意移动性数据，但没有关注V2X消息中的分类数据。因此，本文提出了与多种道路使用者分类数据相关的不良行为分类器。因此，我们比较了包括阈值分类器（MinMax）和三种机器学习算法的四种方法。

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