Systems and methods for dynamic pre-filtering with sampling and caching

A pre-filtering circuit of a vehicle, comprising:

• one or more processors;
• and
• memory operatively connected to the one or more processors, and including instructions that when executed by the one or more processors, cause the pre-filtering circuit to perform:
• sensing first anomalous vehicle-related data;
• analyzing currently collected vehicle-related data to determine existence of second anomalous vehicle-related data;
• attempting to correlate the first and second anomalous vehicle-related data, and if correlatable, analyzing a series of events represented by the first and second anomalous vehicle-related data;
• upon a determination that the series of events warrants further analysis, collecting additional vehicle-related data associated with the first and second anomalous vehicle-related data;
• determining a scenario pattern for the series of events;
• determining, according to the scenario pattern, a a scenario pattern-specific sampling rate for sampling data points collected by a sensor of the vehicle to characterize the series of events using at least one of an extrapolation function or an interpolation function;
• and
• transmitting, to a computing entity performing the at least one of the extrapolation function or the interpolation function, the first and second anomalous vehicle-related data and an amount of the collected additional vehicle-related data collected by the sensor commensurate with the scenario pattern-specific sampling rate.

A vehicle, comprising:

• a communications circuit receiving vehicle-related data;
• a pre-filtering circuit operatively connected to the communications circuit, the pre-filtering circuit:
• determining whether the received vehicle-related data contains first anomalous data relative to a first vehicle operating threshold;
• in response to a determination that the received vehicle-related data contains first anomalous data, triggering collection of additional vehicle-related data;
• determining a scenario pattern for the first anomalous data and the additional vehicle-related data;
• determining, according to the scenario pattern, a scenario pattern-specific sampling rate for sampling data points from the additional vehicle-related data to characterize one or more vehicle-related events represented by the first anomalous data and the additional vehicle-related data using at least one of an extrapolation function or an interpolation function;
• extract, from the additional vehicle-related data, a number of data points commensurate with the scenario pattern-specific sampling rate;
• transmitting only the first anomalous data and the extracted data points to an artificial intelligence system using the at least one of the extrapolation function or the interpolation function to characterize the one or more vehicle-related events.