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.

Methods and systems for testing the operation of autonomous or semi-autonomous operation features in a virtual test environment are provided. Computer-executable instructions for implementing the features may be received and executed on a test device. Test input signals may be presented to software routines associated with the features, which may generate output signals including control commands. The output signals may be used to predict the response of a vehicle in the virtual test environment, which may include a simulation of vehicle responses to the output signals. Measures of the effectiveness of the features may be determined based upon the predicted responses of the vehicle, which may then be used to determine risk levels associated with the features.

A vision system (1) for a motor vehicle, includes a sensing device (40) adapted to sense data (99), a data processing device (7) adapted to process the sensed data (99), wherein the data processing device (7) includes a controller (30) adapted to switch a power mode of the data processing device (7) between a low-power mode and a high-power mode, wherein a high-power application (110) can be executed in the high-power mode. The data processing device (7), in a low-power mode, is adapted to execute a power mode classifier (20), and the power mode classifier (20) is adapted to classify any input data (99) from the sensing device (40) as requiring executing the high-power application (110) or not, and to output a high-power mode request (130) to the controller (30) to switch the power mode of the data processing device (7) from the low-power mode to the high-power mode in case the power mode classifier (20) has classified the input data (99) as requiring executing the high-power application (110).

A method for operating a vehicle. When an autonomous cruise control is in the activated state, a switch is made to an accelerator-pedal-controlled distance controller in response to an acceleration command indicated by an override of an accelerator pedal of the vehicle.

Even in the case that a learning flag and an ACC permission flag are not set, if a power switch is in an ON state, a travel control of a second control state, which is capable of being executed in a setting in which an inter-vehicle distance setting is a short setting or a host vehicle velocity setting is a low setting, is carried out.

An autonomous driving control method according to an embodiment of the present disclosure includes identifying the passenger by monitoring an interior of the vehicle, verifying stress of the passenger, based on that a processor checks a stress history of the passenger or senses biometric information of the passenger in real time, determining a cause of the stress by the processor, based on that the stress of the passenger is verified, and controlling an autonomous driving mode or an autonomous driving level of the vehicle by the processor, based on the cause of the stress. One or more of an autonomous vehicle, a user terminal, and a server of the present disclosure may be in conjunction with an Artificial Intelligence (AI) module, an Unmanned Aerial Vehicle (UAV), an Augmented Reality (AR) device, a Virtual Reality (VR) device, and a device related to a 5G service, etc.

Methods and systems for monitoring use, determining collision risk, and recommending usage levels of one or more autonomous (and/or semi-autonomous) operation features of a vehicle are provided. According to certain aspects, operating data from sensors within the vehicle may be used to determine collision risks associated with use of the features, which may include use at particular levels or with certain settings. The operating data may further be used to determine optimal or suggested use levels for the features. When the actual and suggested use levels differ, an alert may be generated and presented to the vehicle operator indicating suggested changes. The vehicle operator may then change the use levels or select an option to change the usage to the suggested use levels.

An alert control apparatus that notifies a driver in advance of a transfer of control relating to a driving operation from an automatic driving function to the driver by controlling an alert device mounted on a vehicle, which is equipped with the automatic driving function, includes: an estimator that estimates an occurrence of a change execution situation that requires a lane change under a condition in which the driving operation of the vehicle is controlled by the automatic driving function; a determiner that determines a level of difficulty of lane change control based on a plurality of travel environment factors in the change execution situation; and a notification device that notifies the driver of a possibility of the transfer of the control together with a reason of the transfer of the control with a notification mode corresponding to the level using the alert device.

Methods and systems for determining fault for an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features and paying claims associated with such accidents are provided. According to certain aspects, operating data from sensors within or near the vehicle may be used to determine the occurrence of a vehicle accident, such as a collision. The operating data may further be used to determine an allocation of fault for the accident between a vehicle operator, the autonomous operation features, or a third party. The allocation of fault may be used to further determine and make claims payments related to the accident. In some embodiments, claims may be rejected based upon the operating data and determined allocation of fault.

Methods and systems for monitoring use and determining risk associated with operation of autonomous vehicles are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, and operating data regarding vehicle operation may be obtained. The operating data may include information regarding use levels or settings associated with use of the autonomous operation features. Risk levels associated with the features may be determined from test data or actual loss data. The risk levels and operating data may be used to determine a total risk level associated with operation of the vehicle with expected use levels of the autonomous operation features. The total risk level may further be used to determine or adjust aspects of an insurance policy associated with the vehicle.