Abstract: A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

Abstract: A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

Abstract: A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

Abstract: Systems and methods of the disclosed embodiments include an electronic device that has a gate electrode for supplying a gate voltage, a source, a drain, and a channel doped to enable a current to flow from the drain to the source when a voltage is applied to the gate electrode. The electronic device may also include a gate insulator between the channel and the gate electrode. The gate insulator may include a first gate insulator section including a first thickness, and a second gate insulator section including a second thickness that is less than the first thickness. The gate insulator sections thereby improve the safe operating area by enabling the current to flow through the second gate insulator section at a lower voltage than the first gate insulator section.

Abstract: Systems and methods of the disclosed embodiments include an electronic device that has a gate electrode for supplying a gate voltage, a source, a drain, and a channel doped to enable a current to flow from the drain to the source when a voltage is applied to the gate electrode. The electronic device may also include a gate insulator between the channel and the gate electrode. The gate insulator may include a first gate insulator section comprising a first thickness, and a second gate insulator section comprising a second thickness that is less than the first thickness. The gate insulator sections thereby improve the safe operating area by enabling the current to flow through the second gate insulator section at a lower voltage than the first gate insulator section.