Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

Bhadani, Rahul; Bunting, Matt; Nice, Matthew; Tran, Ngoc Minh; Elmadani, Safwan; Work, Dan; Sprinkle, Jonathan

doi:10.1109/di-cps56137.2022.00009

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

Conference · 01 May 2022 · 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS)

DOI:https://doi.org/10.1109/di-cps56137.2022.00009· OSTI ID:1874470

Bhadani, Rahul ^[1]; Bunting, Matt ^[2]; Nice, Matthew ^[3]; Tran, Ngoc Minh ^[4]; Elmadani, Safwan ^[5]; Work, Dan ^[3]; Sprinkle, Jonathan ^[6]

The University of Arizona,Electrical & Computer Engineering,Tucson,Arizona,USA; Vanderbilt University
Institute of Software Integrated Systems Vanderbilt University,Nashville,Tennessee,USA
Vanderbilt University,Civil & Environmental Engineering,Nashville,Tennessee,USA
The University of Arizona,Computer Science,Tucson,Arizona,USA
The University of Arizona,Electrical & Computer Engineering,Tucson,Arizona,USA
Vanderbilt University,Computer Science,Nashville,Tennessee,USA

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

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Research Organization:: University of Arizona

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

DOE Contract Number:: EE0008872

OSTI ID:: 1874470

Journal Information:: 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Journal Name: 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS)

Country of Publication:: United States

Language:: English

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