Lightweight LSTM for CAN Signal Decoding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes an approach to identify undecoded Controller Area Network (CAN) data from one vehicle, based on the data similarity to previously decoded CAN data from another vehicle. Modern vehicles communicate data and signals from on-board sensors and controllers through the CAN bus. Networked sensors contain information such as wheel speeds, fuel gauges, turn signals, and radar signals. In the effort to use this information and make cars safer through human-in-the-loop CPS, signals on the CAN bus such as wheel speed and radar can be used to support the driver. However, data from the CAN bus are encoded and in some cases compressed, and different car manufacturers use different encoding schemes to represent data on the CAN bus. With hundreds of messages and thousands of possible encoding schemes to consider, it is laborious to identify the unique bits and encoding schemes that represent signals on each vehicle. In this study, we propose a method for training a Long Short-Term Memory (LSTM) neural network on known radar signals from one vehicle manufacturer, a Toyota, and successfully apply the network to identify the encoding for radar signals on a different vehicle, a Honda. By augmenting the training dataset with varied encoding bit boundaries, a small and lightweight LSTM network can learn to recognize radar data across different encoding schemes. The results are an improvement on exhaustive-search algorithms and other methods previously used in the search for such signals.

Original languageEnglish (US)
Title of host publicationDICPS 2021 - Proceedings of the ACM 1st Workshop on Data-Driven and Intelligent Cyber-Physical Systems, Part of CPS-IoT Week 2021
PublisherAssociation for Computing Machinery, Inc
Pages27-31
Number of pages5
ISBN (Electronic)9781450384452
DOIs
StatePublished - May 18 2021
Event1st ACM Workshop on Data-Driven and Intelligent Cyber-Physical Systems, DICPS 2021 - Part of CPS-IoT Week 2021 - Virtual, Online, United States
Duration: May 18 2021 → …

Publication series

NameDICPS 2021 - Proceedings of the ACM 1st Workshop on Data-Driven and Intelligent Cyber-Physical Systems, Part of CPS-IoT Week 2021

Conference

Conference1st ACM Workshop on Data-Driven and Intelligent Cyber-Physical Systems, DICPS 2021 - Part of CPS-IoT Week 2021
Country/TerritoryUnited States
CityVirtual, Online
Period5/18/21 → …

ASJC Scopus subject areas

  • Computer Networks and Communications

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