Being able to customize what data is collected and when it's collected is opening up new realms of possibility for automakers.
File logging is a method in which data points are stored in a file and uploaded to the cloud at specified intervals. These intervals can be set and modified by the original equipment manufacturer based on their needs and the desired data. For example, an OEM can set conditions for file upload such as when a certain file size is reached, after a specific amount of time has elapsed or when a unique event occurs—for example, exceeding a speed limit or when a collision is detected.
File logging also enables on-demand uploads where data is logged and stored locally—on the vehicle—and only uploaded if and when the automaker needs it. By reducing the frequency of upload, manufacturers also reduce the costs associated with data transfer. Another benefit to file logging is a higher compression ratio. Rather than sending data in a constant, uncompressed stream, data is efficiently packed into a compressed archive . The behavior of the compression algorithms allows for a better compression ratio so that OEMs can send more data with increased efficiency and lower costs. Once the file reaches the cloud, automakers can unpack it and have access to more data points than they would with live logging.
File logging only uploads a file when it complies with certain requirements. Because of this, the latency is greater than with live logging, and there is a small percentage of use cases where live logging will still be preferred. For example, this latency would make file logging inappropriate for customer-facing mobile applications. However, file logging should be the superior approach for the majority of scenarios., especially when a manufacturer isn't sure where the issue lies.
Achieving compliance with international standards is another use case for file logging. Since many regulations require the logging of different information, OEMs can leverage the customizable parameters enabled by file logging to capture only the data required for the audit. This not only reduces the overall cost of the audit but also helps expedite the process by eliminating unnecessary and distracting data points.and data-driven design improvements.
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