Diagnosing Vehicles with Automotive Batteries
Diagnosing Vehicles with Automotive Batteries
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DOI：
10.1145/3300061.3300126
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Liang He;L. Kong;Ziyang Liu;Yuanchao Shu;Cong Liu
中科院分区：
文献类型：
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作者：
Liang He;L. Kong;Ziyang Liu;Yuanchao Shu;Cong Liu
汽车行业越来越多地采用基于软件的解决方案来为车辆提供增值功能，特别是随着电动汽车和自动驾驶时代的到来。然而，不断增加的车辆网络组件（即计算、通信和控制）会带来新的异常风险，不同制造商召回的数百万辆车辆就证明了这一点。为了减轻这些风险，我们设计了 B-Diag，这是一种基于电池的诊断系统，通过网络物理方法保护车辆免受异常情况的影响，并将 B-Diag 实施为连接到汽车电池的商用车辆的附加模块，从而为车辆提供额外的保护层。 B-Diag 的灵感来自于这样一个事实：汽车电池的运行与车辆的许多物理组件密切相关，这可以通过电池电压与车辆相应运行参数之间的相关性来观察，例如，发动机的每分钟转数 (RPM) 更快，通常会导致更高的电池电压。 B-Diag 基于一组在线构建的数据驱动规范模型，利用这种物理引起的相关性，通过交叉验证车辆信息与电池电压来诊断车辆。 B-Diag 的这种设计是通过原型系统在现实生活中驾驶 2018 款斯巴鲁 Crosstrek 超过 3 个月时收集的数据集进行指导的，总里程约为 1, 400 英里。除了 Crosstrek 之外，我们还使用 2008 款本田飞度、2018 款沃尔沃 XC60 和 2017 款大众帕萨特的驾驶轨迹对 B-Diag 进行了评估，结果显示 B-Diag 检测车辆异常的平均检测率 >86%（高达 99%）。
The automotive industry is increasingly employing software- based solutions to provide value-added features on vehicles, especially with the coming era of electric vehicles and autonomous driving. The ever-increasing cyber components of vehicles (i.e., computation, communication, and control), however, incur new risks of anomalies, as demonstrated by the millions of vehicles recalled by different manufactures. To mitigate these risks, we design B-Diag, a battery-based diagnostics system that guards vehicles against anomalies with a cyber-physical approach, and implement B-Diag as an add-on module of commodity vehicles attached to automotive batteries, thus providing vehicles an additional layer of protection. B-Diag is inspired by the fact that the automotive battery operates in strong dependency with many physical components of the vehicle, which is observable as correlations between battery voltage and the vehicle's corresponding operational parameters, e.g., a faster revolutions-per-minute (RPM) of the engine, in general, leads to a higher battery voltage. B-Diag exploits such physically-induced correlations to diagnose vehicles by cross-validating the vehicle information with battery voltage, based on a set of data-driven norm models constructed online. Such a design of B-Diag is steered by a dataset collected with a prototype system when driving a 2018 Subaru Crosstrek in real-life over 3 months, covering a total mileage of about 1, 400 miles. Besides the Crosstrek, we have also evaluated B-Diag with driving traces of a 2008 Honda Fit, a 2018 Volvo XC60, and a 2017 Volkswagen Passat, showing B-Diag detects vehicle anomalies with >86% (up to 99%) averaged detection rate.