What is Automotive Data Logging?
Automotive data logging is the process of capturing, recording and storing the data collected by vehicle sensors, electronic control units (ECUs) and various communication networks. Depending on the purpose, the collected data may consist of camera images, radar and LiDAR measurements, CAN messages, Automotive Ethernet data, GPS data and vehicle operating parameters.
A data Logger captures information from multiple sources and stories it for further analysis. In ADAS development, data logs can help investigate abnormal system behaviour, reproduce testing scenarios and evaluate vehicle functions performance. Logging solutions capable of handling large amounts of data from multiple sensors at once are especially useful in these cases.
How Does Data Logging Work in an ADAS Applications?
Imagine a test vehicle with cameras, radar, LiDAR, GNSS and a number of communication networks in it. These elements will generate data during a test drive that needs to be acquired and analyzed.
The common workflow in data logging includes data acquisition from sensors, communication over supported interfaces, time synchronization, recording of the data and its storage. The recorded may then be transmitted to engineering teams for further analysis, algorithms development and validation.
In the b-plus Automotive devices and tools, we have some b-plus architectures. There are solutions such as basic models like LOGifyer B1 SE and it has high configurable models also like BRICK 2 X11 that enable data logging, whereas MDILink can be helpful in decoupling of supported high speed as MDLake support management of large amounts of recorded data. Configuration would depend upon the type of sensors interfaces and data rate and recording capabilities.
Benefits of Automotive Data Logging
These are several advantages of using automotive data logging. With the use of data logging, engineers can analyze real- world driving conditions, check intermittent problems, compare behavior of the system across several runs and validate the performance of ADAS features.
Recording datasets are helpful in development of data driven workflow such as dataset generation and analysis for machine learning purpose. Timestamps and data recording are essential aspects of accurate data correlation between sensors.
However, proper planning is necessary for the success of a logging solution. It is necessary for the engineer to evaluate several aspects such as data bandwidth, storage, interfaces, synchronization, logging duration and data integrity. Proper hardware and software selection ensures proper data logging.
With Modrem automotive systems becoming more data-heavy, it is increasingly important to have a good data logging system in place for the development and testing of ADAS.
We assist our customers in exploring the b-plus technology available for automotive data logging, fast sensor data transfer, data logging and storage.
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