Abstract: Systems, methods, and apparatuses are provided for evaluating the calibration requirements or calibration needs of one or more sensors of a vehicle. A subject matter expert and/or a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CIECA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair. In some examples, a machine learning model can cluster or otherwise identify a likely area of repair based on information embedded or contained in a repair estimate.

Abstract: Systems, methods, and apparatuses are provided for evaluating the calibration requirements or calibration needs of one or more sensors of a vehicle. A subject matter expert and/or a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair. In some examples, a machine learning model can cluster or otherwise identify a likely area of repair based on information embedded or contained in a repair estimate.