Cerebrumx Labs Private Limited

Sandip Ranjhan

Amit Gupta

Sarika Gupta

The present invention provides a robust and effective solution to an entity or an organization by allowing aggregation of a set of automotive data packets pertaining to single trip using one or more common identifiers and one or more timestamps to enable enhanced data analytics and data correlation. The set of automotive data packets may be then segregated based on a plurality of sensor signals and a plurality of calculated signals to obtain a set of derived signals. The set of derived signals can provide an option of moving calculation of derived signals outside of vehicle to reduce computation cost on the vehicle. The size of the data packets may be optimized by sending the plurality of sensor signals outside of minimum signal set, only when one or more values change from the last stored value.

System and method facilitating harmonizing of automotive signals

The present invention provides a robust and effective solution to an entity or an organization for creating and standardizing an Automotive Data Quality Marker (ADQM) to determine/evaluate/predict the quality of automotive data such as Telematics, Body Control, ADAS, Diagnostics, Dashcams, and In-Vehicle Infotainment but not limited to the like generated by the vehicle (i.e., data source) using a machine learning (ML) engine associated with a processing unit. The machine learning engine comprises an amalgamation of machine learning algorithms to determine ADQM for a particular dataset. Data pertaining to vehicles is huge and repetitive. Re-training of the model for improved accuracy is a requirement as automotive data can be augmented with additional signals and data received and stored as trip objects on a regular basis.

Method and system for auto generating automotive data quality marker

The present invention provides a robust and effective solution to an entity or an organization by allowing to visualize the flow of automotive data signals from a source system to a sink system with different metric signals at each step and creating a mechanism for creating automotive signal quality marker (ASQM) for high quality automotive data signals by determining high value data parameters, inter-parameter correlation, data quality variation with trip duration, and frequency of data collection/distribution across datasets. The quality criterion thus developed is applied to data generated from a plurality of data sources. The ASQM may be calculated by converting the developed automotive data signals quality criterion into a numeric value on a specified scale. It can be used by all stakeholders in the value chain for consistent measurement of automotive data signals quality.

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Delhi, India

Sandip Ranjhan