by Paul Ngo 1,* , Jonathan Sprinkle 2 , Rahul Bhadani 3
1 University of California, Berkeley, Berkeley, California, USA, 94704
2 Vanderbilt University, Nashville, Tennessee, USA, 37212
3 The University of Arizona, Tucson, Arizona, USA, 85721
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 10, Page # 5-12, 2022; DOI: 10.55708/js0110002
Keywords: External interfaces for robotics, Computing methodologies, Learning paradigms, Neural
networks
Received: 19 July 2022, Accepted: 21 September 2022, Published Online: 10 October 2022
AMA Style
Chicago/Turabian Style
IEEE Style
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.
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