“My autonomous car is an elephant”: A Machine Learning based Detector for Implausible Dimension
“My autonomous car is an elephant”: A Machine Learning based Detector for Implausible Dimension
复制标题
“我的自动驾驶汽车是一头大象”：基于机器学习的难以置信维度探测器
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Alain Servel
中科院分区：
文献类型：
--
作者：
J. Monteuuis;J. Petit;Jun Zhang;H. Labiod;Stefano Mafrica;Alain Servel
互联和自动驾驶汽车是汽车制造商在交通安全和效率方面的下一个目标。为了确保安全，汽车应用依赖于通过车载通信和本地嵌入式传感器获取的数据。在这些数据中，分类数据允许自动驾驶汽车不仅根据其动态，而且根据其长度和宽度来决定是否超过另一辆汽车。与容易产生测量误差的传感器不同，车载通信允许其他联网车辆根据汽车制造商的规格提供准确的尺寸值。然而，这一事实的前提是其他道路使用者可能不会撒谎。目前，研究人员主要关注恶意移动数据，但没有人关注V2X消息中的分类数据。因此，本文提出了一种与多类型道路使用者分类数据相关的不当行为分类器。因此，我们比较了四种方法，包括阈值分类器（MinMax）和三种机器学习算法。
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.