Abstract: This application discloses a computing system to self-diagnosis of faults for an assisted or automated driving system of a vehicle. The computing system can populate measurement data collected by sensors mounted in the vehicle into an environmental model, and detect an object proximate to the vehicle based on the measurement data in the environmental model. The computing system can identify a fault in at least one of the sensors by performing one or more safety cross-checks including one or more of comparing the environmental modal against one or more data models populated with sensor measurements or objects detected by different sensors, analyzing estimated motion of the vehicle or the object for aberrant movement, identifying missing measurement data, identifying divergent object classifications, or identifying operational sensor characteristics. A control system for the vehicle can configured to control operation of the vehicle based, at least in part, on the identified fault.

Abstract: This application discloses training of a classification system for an assisted or automated driving system of a vehicle. A processing system can label sensor measurement data collected by sensors mounted in the vehicle with classifications, which can include a type of an object associated with the sensor measurement data and a confidence level of the classification. A training system can utilize the classifications labeled to the sensor measurement data to train a classification graph utilized by the classification system. The training system can select a node in a classification graph based, at least in part, on a classification labeled to sensor measurement data. The training system can compare the sensor measurement data to matchable data in the selected node, and modify the classification graph based, at least in part, on differences between the sensor measurement data and the matchable data in the selected node.