An abnormality of a component can be accurately detected.

An on-vehicle device is an on-vehicle device that is mounted on a vehicle to communicate with a server. The on-vehicle device includes a first information generation unit that generates first information which is information on a state of a first component mounted on the vehicle by using an output of a sensor mounted on the vehicle, and an out-vehicle communication unit that transmits, to the server, the first information and second information which is information on a state of a second component which is a component different from the first component, and receives, from the server, a state signal indicating an abnormal state of the first component calculated by the server based on the first information and the second information.

Embodiments of the present disclosure include automated guided vehicles (AGVs) having a broadcasting system. In one embodiment, the self-driving system includes a body having one or more motorized wheels, a console coupled in an upright position to an end of the body, and a broadcasting system disposed at the console and is operable to send a notification to one or more mobile devices, wherein the broadcasting system uses a first type of positioning system and the one or more mobile devices use a second type of positioning system different from the first type of positioning system.

A method is provided for automated application (10) of a driver assistance system that is configured to implement automated driving functions. At least one application parameter is assigned to each automated driving function (12). Factory settings preset both the application parameter and acceptable ranges for the application parameters that are consistent with safety-critical requirements. A control unit identifies a relevant driving scenario after an automated driving function (12) has been implemented during normal driving operation (13, 14). The control unit uses an objective grading model to evaluate (17) a performance of each implemented automated driving function while continuing execution during a normal driving operation (13, 14). The at least one respectively assigned application parameter is adapted (15), as a result of an optimization (11), on the basis of the evaluation (17) of the performance of the respectively implemented automated driving function (12).

A computer-implemented system and method is provided for determining a risk assessment. The method comprises receiving a plurality of vehicle behaviour data over a defined data collection period. This data is input into a supervised learning prediction model which is trained on historical vehicle behaviour data over a past time period, to generate a predicted value of a frequency of expected claim submissions for the policyholder of the vehicle in a future time period. Then a Shapley estimate is computed for each feature of the behaviour data applied to the model for determining a contribution of each said feature to the predicted value. A spline approximation is applied to the Shapley estimate for each said feature to estimate the contribution of each said feature. Then, a sum of the spline approximation for each said feature is calculated and a corresponding risk score determined based on the sum.

An apparatus, method and computer program product provide a map layer of one or more temporary dynamic obstructions. For example, the apparatus is configured to receive vehicle/driver behavior data associated with a vehicle at a portion of a road, determine a likelihood of a temporary dynamic obstruction existing proximate to the portion based on the vehicle behavior data, and update a datapoint of a map layer based on the likelihood. The datapoint indicates a state of existence of the temporary dynamic obstruction at the portion.

In one aspect, a device includes at least one processor and storage accessible to the at least one processor. The storage includes instructions executable by the at least one processor to receive at least one override signal of at least a first autonomous driving feature of a vehicle, disable the first autonomous driving feature responsive to the override signal, and wirelessly transmit to at least a first computer server a signal indicating that the override signal was received.

The present disclosure describes a method for monitoring operations of an automated driving system (ADS) of a vehicle. For each monitored operation the method includes: determining a geographical position of the vehicle; determining an intended path of the vehicle; and determining one or more intended parameters associated with performing a driving manoeuvre of said vehicle from the determined geographical position along the intended path. For each monitored operation the method further includes: obtaining one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position; and retrieving, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path. Based on said retrieved data, determining whether there is an anomaly associated with said monitored operation; and taking at least one action of a set of predefined actions if an anomaly is determined.

A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

A vehicle control method and device are disclosed. An embodiment of the method includes: receiving an assistance driving instruction associated with a vehicle; and sending an assistance control instruction to the vehicle, to cause the vehicle to execute the assistance control instruction to control the vehicle, the assistance control instruction being generated through a simulation driving operation performed by an assistance driving user based on travelling environment information of the vehicle presented to the assistance driving user.

An information processing device that receives, from each of several vehicles, both image information captured by an image capture device installed at the vehicle, and vehicle information including position information on the vehicle; performs image processing identifying a characteristic of a dangerously-driven vehicle based on the image information; and in a case in which a dangerously-driven vehicle has been detected, modifies a priority level for image processing of the image information received from another vehicle in the vicinity of a vehicle that has detected the dangerously-driven vehicle, so as to be higher than for a vehicle not in the vicinity of the vehicle that has detected the dangerously-driven vehicle.