CANClassify: Automated Decoding and Labeling of CAN Bus Signals
CANClassify: Automated Decoding and Labeling of CAN Bus Signals
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CANClassify：CAN 总线信号的自动解码和标记
DOI：
10.55708/js0110002
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
R. Bhadani
中科院分区：
文献类型：
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作者：
P. Ngo;J. Sprinkle;R. Bhadani
：目前，大多数车辆都使用控制器区域网络(CAN)总线数据来报告和通信传感器数据。然而，该数据通常是编码的，不能简单地通过查看总线上的原始数据来直接解释。然而，通过利用有关信号如何编码的知识并使用独立记录的地面真实信号值进行相关，可以对CAN总线数据进行解码并对编码进行反向工程。虽然存在支持对可能的信号进行解码的方法，但这些方法通常需要额外的人工工作来标记每个信号的功能。本文提出了一种利用卷积解译方法对CAN报文进行预处理的CANategfy方法，该方法接收原始的CAN总线数据，并自动对CAN总线信号进行解码和标记。我们在一个先前未解码的车辆上评估了CANategfy的性能，并手动确认了编码。我们展示了堪比最先进水平的性能，同时还提供了自动标签。示例和代码可在https://github.com/ngopaul/CANClassify上找到。
: Controller Area Network (CAN) bus data is used on most vehicles today to report and communicate sensor data. However, this data is generally encoded and is not directly interpretable by simply viewing the raw data on the bus. However, it is possible to decode CAN bus data and reverse engineer the encodings by leveraging knowledge about how signals are encoded and using independently recorded ground-truth signal values for correlation. While methods exist to support the decoding of possible signals, these methods often require additional manual work to label the function of each signal. In this paper, we present CANClassify — a method that takes in raw CAN bus data, and automatically decodes and labels CAN bus signals, using a novel convolutional interpretation method to preprocess CAN messages. We evaluate CANClassify’s performance on a previously undecoded vehicle and confirm the encodings manually. We demonstrate performance comparable to the state of the art while also providing automated labeling. Examples and code are available at https://github.com/ngopaul/CANClassify .
更安全的自适应巡航控制系统可抑制交通波
DOI：
10.1145/3450267.3452003
发表时间：
2021
期刊：
Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems
影响因子：
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作者：
Baschab, Emily;Ball, Savannah;Vazzana, Audrey;Sprinkle, Jonathan
通讯作者：
Sprinkle, Jonathan
CAN coach：通过人类网络物理系统进行车辆控制
DOI：
10.1145/3450267.3450541
发表时间：
2021
期刊：
ICCPS '21: Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems
影响因子：
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作者：
Nice, Matthew;Elmadani, Safwan;Bhadani, Rahul;Bunting, Matt;Sprinkle, Jonathan;Work, Dan
通讯作者：
Work, Dan