An evaluating apparatus is provided with: a first acquirer configured to obtain a feature value indicating driving behavior of a driver; a classifier configured to classify a plurality of feature values obtained from a plurality of drivers, into a plurality of groups; a second acquirer configured to obtain the feature value that is representative in each of the plurality of groups, as a representative feature value; a ranking device configured to give a rank corresponding to a driving carefulness degree, to each of the plurality of groups, on the basis of the representative feature value; and a determinator configured to determine a driver type corresponding to the driving carefulness degree of the driver, on the basis of a rank of a group into which the feature value of the driver is classified.

Aspects of the disclosure relate to computing platforms that utilize improved techniques for dynamic event verification. A computing platform may receive first source data comprising driving data associated with a vehicle over a time period. Based on the first source data, the computing device may determine that the vehicle experienced an event, resulting in an event output. In response to determining the event output, the computing device may generate a request for second source data associated with the vehicle over the time period. The computing device may receive, from a sensor device, the second source data. Based on a comparison of the first source data to the second source data, the computing platform may determine an event comparison output. The computing platform may determine that the event comparison output exceeds a predetermined comparison threshold, and may send an indication of an event in response.

The present application discloses a display panel and a display device. The display panel includes: a common electrode layer including a plurality of columns of first common electrodes, wherein each column of the plurality of columns of the first common electrodes includes a plurality of touch electrodes insulated from each other; and a driving module. Each of the plurality of touch electrodes is electrically connected to the driving module through one or more touch leads. A number of the touch leads corresponding to each of or adjacent ones of the plurality of touch electrodes gradually increases along a direction away from the driving module.

Aspects of the disclosure relate to computing platforms that utilize machine learning to reduce false positive/negative collision output generation. A computing platform may apply machine learning algorithms on received data to generate a collision output. In response to generating the collision output indicating a collision, the computing platform may identify a data collection location. If the data collection location is within a predetermined radius of a false positive collection location, the computing platform may modify the collision output to indicate a non-collision. If the data collection location is not within the predetermined radius, the computing platform may compute a score using telematics data and compare the score to a predetermined threshold. If the score does not exceed the predetermined threshold, the computing platform may modify the collision output to indicate a non-collision. If the score exceeds the predetermined threshold, the computing platform may affirm the collision output indicating a collision.

A system for estimating flight range of an electric aircraft. The system generally includes at least a sensor and a computing device. The at least a sensor is communicatively connected to at least a flight component. The at least a sensor is configured to detect a performance datum of the at least a flight component. The computing device is communicatively connected to the at least a sensor. The computing device is configured to receive the performance datum from the at least a sensor, determine an energy performance datum from the performance datum, determine a flight performance datum from the performance datum, generate a projected flight range datum as a function of the energy performance datum and the flight performance datum, and display the projected flight range datum. A method for estimating flight range of an electric aircraft is also provided.

A method for indirectly deriving a systematic dependence for a system behavior of a system component of a cleaning system of a motor vehicle. The cleaning system is adapted for cleaning at least one surface of the motor vehicle by means of a cleaning process adapted for a resource efficient cleaning. Additionally, a diagnosing method, a method for selecting a resolution strategy, a use of a resolution strategy, a cleaning method, a cleaning system, and a motor vehicle are disclosed.

The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a bounded-error estimator system. The method includes receiving information about a plurality of vehicle states of a vehicle from at least one sensor, determining that the information is missing data about at least one vehicle state of the plurality of vehicle states, and determining an estimated vehicle state associated with a final vehicle state. Determining the estimated vehicle state includes calculating a plurality of augmented states for each of the vehicle states included in the plurality of vehicle states and calculating the estimated vehicle state based on the plurality of augmented states. The estimated vehicle state is provided to a vehicle control system of the vehicle.

Systems, methods and apparatus of recordation of vehicle data associated with errant vehicle behavior. For example, a vehicle includes: sensors configured to generate sensor data; control elements configured to generate control signals to be applied to the vehicle in response to user interactions with the control elements; electronic control units configured to provide status data in operations of the electronic control units; and a data storage device. The data storage device is configured to receive input data including the sensor data, the control signals and the status data, store the input data in a cyclic way in an input partition over time, generate a classification of errant behavior based on the input data and using an artificial neural network, and preserve a portion of the input data associated with the classification of errant behavior.

In one embodiment, a control command is generated by an autonomous controller of the ADV. Feedback is sensed that corresponds to the control command. A difference is determined between a) the control command, and b) the feedback corresponding to the control command. If the difference is meets a threshold, then a fault response is generated.

A method for detecting abnormal operating states of a device includes obtaining model data to the device that is representative of operating states to be expected for at least one component of the device. The device collects measurement data that is representative of an actual operating state of the component of the device. The device ascertains comparison data on the basis of the model data and the measurement data, where the comparison data is representative of an expected operating state. The method includes using the comparison data and the measurement data as a basis for determining whether there is a discrepancy between the actual operating state and the expected operating state. The method further includes attributing an abnormal operating state to the at least one component in a manner corresponding to a time of collection of the measurement data on the basis of the discrepancy.