Systems and methods for automated vehicle sensor calibration and verification are provided. One example method involves monitoring a vehicle using one or more external sensors of a vehicle calibration facility. The sensor data may be indicative of a relative position of the vehicle in the vehicle calibration facility. The method also involves causing the vehicle to navigate in an autonomous driving mode, based on the sensor data, from a current position of the vehicle to a first calibration position in the vehicle calibration facility. The method also involves causing a first sensor of the vehicle to perform a first calibration measurement while the vehicle is at the first calibration position. The method also involves calibrating the first sensor based on at least the first calibration measurement.

A method and a system determine a change of state of an autonomous device, such as an autonomous vehicle. A plurality of performance parameter values obtained by monitoring at least one performance parameter during the autonomous operation of the device is received. A performance quantity quantifying the quality of autonomous operation of the device, in particular the quality of driving of the autonomous vehicle, is determined based on the obtained performance parameter values and information associated with a flux of software and/or hardware related to the autonomous operation of the device. Further, a change of state value for the device is determined based on the performance quantity.

A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation. A perception model can be trained using the synthetic image in response to determining that the objective function is within a predetermined acceptable range.

A method for performing automatic valet parking, which includes selecting a road scenario applicable to a roadway; notifying a driver to release manual control elements of a motor vehicle and to leave the motor vehicle; checking whether the control elements have been released and the driver has left the motor vehicle and, in this case, entering an EXPLORE mode in which the motor vehicle is slowly driven autonomously and searches for a free car space or a parking space using the vehicle's own environmental sensors, before the motor vehicle is placed in a parking position; and then to change from the EXPLORE mode to a PARKING mode in which the motor vehicle is parked in the car space or in the parking space from the parking position by means of the longitudinal and lateral controllers and using the environmental data previously obtained from the environmental sensors in the EXPLORE mode.

An on-board computing system for a vehicle is configured to generate and selectively present a set of autonomous-switching directions within a navigation user interface for the operator of the vehicle. The autonomous-switching directions can inform the operator regarding changes to the vehicle's mode of autonomous operation. The on-board computing system can generate the set of autonomy-switching directions based on the vehicle's route and other information associated with the route, such as autonomous operation permissions (AOPs) for route segments that comprise the route. The on-board computing device can selectively present the autonomy-switching directions based on locations associated with anticipated changes in autonomous operations determined for the route of the vehicle, the vehicle's location, and the vehicle's speed. In addition, the on-board computing device is further configured to present audio alerts associated with the autonomy-switching directions to the operator of the vehicle.

A method for controlling in an automated manner a motor vehicle (10) traveling on a road (12) in a current lane (14) is suggested, wherein the road (12) has at least one further lane (16). The method comprises the following steps: At least two preliminary driving maneuvers are generated and/or received, which include a lane change from the current lane (14) to the at least one further lane (16) and a starting time of the lane change. The starting times of the at least two preliminary driving maneuvers are at different times. The at least two driving maneuvers are compared taking into account the respective starting times. One of the starting times is selected based on the comparison. Further, a control device for a system for controlling a motor vehicle is also suggested.

A processing apparatus includes a control unit. The control unit is configured to acquire facility information containing an advertisement or publicity on a facility located along a travel route that a vehicle is scheduled to travel or a facility located within a predetermined range from the travel route, and, while the vehicle is traveling along the travel route, process an image of a first facility associated with the facility information or an image of a second facility present around the first facility based on the facility information and display the image of the first facility or the image of the second facility on a display provided in the vehicle.

An occlusion detection system for an autonomous vehicle is described herein, where a signal conversion system receives a three-dimensional sensor signal from a sensor system and projects the three-dimensional sensor signal into a two-dimensional range image having a plurality of pixel values that include distance information to objects captured in the range image. A localization system detects a first object in the range image, such as a traffic light, having first distance information and a second object in the range image, such as a foreground object, having second distance information. An occlusion polygon is defined around the second object and the range image is provided to an object perception system that excludes information within the occlusion polygon to determine a configuration of the first object. A directive is output by the object perception system to control the autonomous vehicle based upon occlusion detection.

A vehicle includes a brake device; a storage configured to store a first setting value and a second setting value having a smaller magnitude than the first setting value; a communicator configured to receive an automatic parking signal; a detector configured to detect at least one of an object or whether the vehicle is in contact with the object; and a controller configured to control the brake device based on a detection result of the detector and the first setting value, and to control the brake device based on the detection result and the second setting value when the automatic parking signal is received.

According to an aspect of an embodiment, operations may comprise receiving a plurality of frame sets generated while navigating a local environment, receiving an occupancy map (OMap) representation of the local environment, for each of the plurality of frame sets, generating, using the OMap representation, one or more instances each comprising a spatial cluster of neighborhood 3D points generated from a 3D sensor scan of the local environment, and classifying each of the instances as dynamic or static, tracking instances classified as dynamic across the plurality of frame sets using a tracking algorithm, assigning a single instance ID to tracked instances classified as dynamic across the plurality of frame sets, estimating a bounding box for each of the instances in each of the plurality of frame sets, and employing the instances as ground truth data in a training of one or more deep learning classifiers.