CAN-DAQ: An open-source, cost-effective data capture device and software for automotive research

Area of Science:

• Automotive Engineering
• Embedded Systems
• Data Acquisition

Background:

• Modern vehicles and industrial systems generate extensive Controller Area Network (CAN) bus data.
• Existing tools for CAN data capture and analysis are often expensive commercial products or immature open-source alternatives.
• Access to affordable, open-source tools is crucial for validating subsystems, developing ECUs, and performing network security analysis.

Purpose of the Study:

• To present CAN-DAQ, an integrated hardware-software platform addressing the need for affordable, open-source CAN data acquisition and analysis.
• To provide a cost-effective alternative to commercial systems with comparable core features.
• To enable researchers and developers to build custom real-time data analytics applications.

Main Methods:

• Development of an ESP32-based hardware interface for CAN bus communication.
• Creation of a flexible Python-based software with an SDK for data analysis and visualization.
• Implementation of a robust SQL backend for data storage.
• Support for classic CAN baud rates (25 kbps to 1 Mbps) and a maximum sampling frequency of 1 kHz.

Main Results:

• CAN-DAQ reliably captures 1000 CAN frames per second.
• The platform provides high-resolution real-time visualization of CAN data.
• Validation testing confirmed reliable data reception, transmission, and sampling capabilities comparable to commercial interfaces.
• Demonstrated a cost-effective solution for accessing and analyzing CAN bus data.

Conclusions:

• CAN-DAQ successfully bridges the gap in affordable, open-source CAN data acquisition.
• The platform empowers users with a foundation for custom real-time data analytics.
• It offers a viable, cost-efficient solution for automotive and industrial research and development.