In various examples, a trigger signal may be received that is indicative of a vehicle maneuver to be performed by a vehicle. A recommended vehicle trajectory for the vehicle maneuver may be determined in response to the trigger signal being received. To determine the recommended vehicle trajectory, sensor data may be received that represents a field of view of at least one sensor of the vehicle. A value of a control input and the sensor data may then be applied to a machine learning model(s) and the machine learning model(s) may compute output data that includes vehicle control data that represents the recommended vehicle trajectory for the vehicle through at least a portion of the vehicle maneuver. The vehicle control data may then be sent to a control component of the vehicle to cause the vehicle to be controlled according to the vehicle control data.

In various examples, a trigger signal may be received that is indicative of a vehicle maneuver to be performed by a vehicle. A recommended vehicle trajectory for the vehicle maneuver may be determined in response to the trigger signal being received. To determine the recommended vehicle trajectory, sensor data may be received that represents a field of view of at least one sensor of the vehicle. A value of a control input and the sensor data may then be applied to a machine learning model(s) and the machine learning model(s) may compute output data that includes vehicle control data that represents the recommended vehicle trajectory for the vehicle through at least a portion of the vehicle maneuver. The vehicle control data may then be sent to a control component of the vehicle to cause the vehicle to be controlled according to the vehicle control data.

A controller causes a host vehicle to decelerate when a velocity of an oncoming vehicle corresponding to a position of the oncoming vehicle distant from a stationary object by a predetermined distance is greater than or equal to a velocity threshold corresponding to the position of the oncoming vehicle distant from the stationary object by the predetermined distance in a case in which a passing position is present within a predetermined region, and causes the host vehicle to keep the velocity or accelerate when the velocity of the oncoming vehicle corresponding to the position of the oncoming vehicle distant from the stationary object by the predetermined distance is less than the velocity threshold corresponding to the position of the oncoming vehicle distant from the stationary object by the predetermined distance in the case in which the passing position is present within the region.

A vehicle for traversing an area with a minimal environmental impact is described. The vehicle includes a first component that creates a first environmental impact when the vehicle is traversing in the area. The vehicle further includes a second component configured to reduce the first environmental impact.

A vehicle for traversing an area with a minimal environmental impact is described. The vehicle includes a first component that creates a first environmental impact when the vehicle is traversing in the area. The vehicle further includes a second component configured to reduce the first environmental impact.

The present disclosure relates to a method of driving a motor vehicle. The method determines position data relating to at least one of a current position or a predicted future position of the motor vehicle detecting, using at least one sensor device, surroundings data relating to a surrounding environment of the motor vehicle, determining at least one driving intervention based on the surroundings data, and controlling at least one vehicle system of the motor vehicle to execute the determined at least one driving intervention. The at least one driving intervention executed by a selected software module that is selected based on the surroundings data, and the selected software module is selected from a plurality of software modules based on the position data. Each software module is configured to execute the at least one driving intervention.

A method for detecting forgery in facial recognition is disclosed that comprises selecting (110) an illumination pattern including at least three illumination intensities; controlling (120) a light-emitting device (220) to emit light pulses, according to the illumination pattern; capturing (130) with a camera (210) a respective image of an eye region (15) of a face (10) during each emitted light pulse; determining (140) whether a glint (20) is present in the eye region (15) of each captured image; measuring (150) a glint intensity of each determined glint (20); extracting (160) a respective numerical feature for each captured image, the numerical feature representing a curve of all measured glint intensities at the glint intensity of the respective image; and outputting (180) a signal that the face (10) is forged, if one or more of the extracted numerical features does not correspond to a reference numerical feature. Furthermore, a computer-readable medium storing instructions to perform the method, and a vehicle (1) comprising a processor (250) capable of performing such method.

A method for detecting forgery in facial recognition is disclosed that comprises selecting (110) an illumination pattern including at least three illumination intensities; controlling (120) a light-emitting device (220) to emit light pulses, according to the illumination pattern; capturing (130) with a camera (210) a respective image of an eye region (15) of a face (10) during each emitted light pulse; determining (140) whether a glint (20) is present in the eye region (15) of each captured image; measuring (150) a glint intensity of each determined glint (20); extracting (160) a respective numerical feature for each captured image, the numerical feature representing a curve of all measured glint intensities at the glint intensity of the respective image; and outputting (180) a signal that the face (10) is forged, if one or more of the extracted numerical features does not correspond to a reference numerical feature. Furthermore, a computer-readable medium storing instructions to perform the method, and a vehicle (1) comprising a processor (250) capable of performing such method.

Methods and systems for a remote transportation system including a first autonomous vehicle, at least one second autonomous vehicle and a remote transportation server are provided. The server includes non-transitory computer readable media and one or more processors configured by programming instructions on the non-transitory computer readable media to: receive a request for tow service from the first vehicle, wherein the request includes a location of the first autonomous vehicle; identify a towing destination for the first autonomous vehicle based on the location of the first autonomous vehicle; perform an initial fault diagnosis to determine a towing type; confirm the towing type with the first autonomous vehicle to be at least one of a ground tow, an aerial tow, and a sensor kit tow; and coordinate towing of the first autonomous vehicle to the towing destination by the at least one second autonomous vehicle based on the towing type.

The present teaching relates to method, system, and medium, for operating a vehicle. Real-time data related to the vehicle are received. A current mode of operation of the vehicle and a state of the driver present in the vehicle are determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data and the state of the driver in accordance with a risk model. In response to the first risk satisfying a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined based on the state of the driver. The vehicle is switched from the current mode to the different mode when the second risk satisfies a second criterion.