There is provided an automated valet parking server that causes an autonomous driving vehicle in a parking place to perform automated valet parking by instructing the autonomous driving vehicle. The automated valet parking server includes an illuminance information acquisition unit configured to acquire illuminance information of front lighting devices of the autonomous driving vehicle, an illuminance suppression point setting unit configured to set an illuminance suppression point which is a position in the parking place at which illuminance of the front lighting devices of the autonomous driving vehicle is suppressed based on parking place map information of the parking place and the illuminance information of the front lighting devices of the autonomous driving vehicle, and an illuminance suppression instruction unit configured to instruct the autonomous driving vehicle to perform illuminance suppression at the illuminance suppression point.

Devices, systems, and methods for remote control of autonomous vehicles are disclosed herein. A method may include receiving, by a device, first data indicative of an autonomous vehicle in a parking area, and determining, based on the first data, a location of the autonomous vehicle. The method may include determining, based on a the location, first image data including a representation of an object. The method may include generating second image data based on the first data and the first image data, and presenting the second image data. The method may include receiving an input associated with controlling operation of the autonomous vehicle, and controlling, based on the input, the operation of the autonomous vehicle.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a representation of a trajectory of a target agent in an environment. In one aspect, the representation of the trajectory of the target agent in the environment is a concatenation of a plurality of channels, where each channel is represented as a two-dimensional array of data values. Each position in each channel corresponds to a respective spatial position in the environment, and corresponding positions in different channels correspond to the same spatial position in the environment. The channels include a time channel and a respective motion channel corresponding to each motion parameter in a predetermined set of motion parameters.

The present disclosure provides a three-dimensional (3D) object detection method, a 3D object detection apparatus, an electronic device, and a readable storage medium, belonging to a field of computer vision technologies. Two-dimensional (2D) image parameters and initial 3D image parameters are determined for a target object. Candidate 3D image parameters are determined for the target object based on a disturbance range of 3D parameters and the initial 3D image parameters determined for the target object. Target 3D image parameters are selected for the target object from the candidate 3D image parameters determined for the target object based on the 2D image parameters. A 3D detection result of the target object is determined based on the target 3D image parameters.

Systems can be configured for detecting license plates and recognizing characters in license plates. In an example, a system can receive an image and identify one or more regions in the image that include a license plate. Character recognition can be performed in the one or more regions to determine contents of a candidate license plate. Location-specific information about a license plate format can be used together with the determined contents of the candidate license plate to determine if the recognized characters are valid.

Systems can be configured for detecting license plates and recognizing characters in license plates. In an example, a system can receive an image and identify one or more regions in the image that include a license plate. Character recognition can be performed in the one or more regions to determine contents of a candidate license plate. Location-specific information about a license plate format can be used together with the determined contents of the candidate license plate to determine if the recognized characters are valid.

The present invention generally relates to a technology of accurately mapping a specific point in CCTV video onto the corresponding point on a map. More specifically, the present invention relates to a technology of mapping a specific point in CCTV video onto the corresponding point on a map through coordinate transformation between pixel coordinate of canvas plane of the CCTV camera and global coordinate of physical space for the video surveillance system. The present invention may provide an advantage of improving the efficiency of video surveillance system. When an urgent situation is found in CCTV video, the location in which the urgent situation is occurring may be accurately specified, which renders an effective action possible.

The disclosure provides an information processing method for a vehicle driving on a curve. The method includes: obtaining a monitored image of a curve; identifying an image position of a first vehicle driving on the curve in the monitored image; determining a target geographic position of the first vehicle according to the image position; and generating prompt information according to the target geographic position of the first vehicle and sending the prompt information, in which, the prompt information is configured to determine vehicle spacing information according to the target geographic position of the first vehicle and a geographic position of a second vehicle receiving the prompt information, and issue a warning based on the vehicle spacing information.

In various examples, a set of object trajectories may be determined based at least in part on sensor data representative of a field of view of a sensor. The set of object trajectories may be applied to a long short-term memory (LSTM) network to train the LSTM network. An expected object trajectory for an object in the field of view of the sensor may be computed by the LSTM network based at least in part an observed object trajectory. By comparing the observed object trajectory to the expected object trajectory, a determination may be made that the observed object trajectory is indicative of an anomaly.

In various examples, a set of object trajectories may be determined based at least in part on sensor data representative of a field of view of a sensor. The set of object trajectories may be applied to a long short-term memory (LSTM) network to train the LSTM network. An expected object trajectory for an object in the field of view of the sensor may be computed by the LSTM network based at least in part an observed object trajectory. By comparing the observed object trajectory to the expected object trajectory, a determination may be made that the observed object trajectory is indicative of an anomaly.