In one embodiment, a method is provided. The method comprises determining a first value of a coefficient of an edge-determining algorithm in response to a spatial resolution of a first image acquired with an image capture device onboard a vehicle, a spatial resolution of a second image, and a second value of the coefficient in response to which the edge-determining algorithm generated a second edge map corresponding to the second image. The method further comprises determining, with the edge-determining algorithm in response to the coefficient having the first value, at least one edge of at least one object in the first image. The method further comprises generating, in response to the determined at least one edge, a first edge map corresponding to the first image. The method further comprises determining at least one navigation parameter of the vehicle in response to the first and second edge maps.

System, methods, and other embodiments described herein relate to training a depth model for joint depth completion and prediction. In one arrangement, a method includes generating depth features from sparse depth data according to a sparse auxiliary network (SAN) of a depth model. The method includes generating a first depth map from a monocular image and a second depth map from the monocular image and the depth features using the depth model. The method includes generating a depth loss from the second depth map and the sparse depth data and an image loss from the first depth map and the sparse depth data. The method includes updating the depth model including the SAN using the depth loss and the image loss.

A calibration support includes a support body (100) configured to mount a calibration element, the calibration element being configured to calibrate a driving assistance system of a vehicle (500); an image acquisition device (200) connected to the support body (100) and configured to acquire an image of the vehicle (500); a processing device (300) provided on the support body (100), electrically connected to the image acquisition device (200), and configured to calculate, according to the image acquired by the image acquisition device (200), the movement position of the support body (100) relative to the vehicle (500) and output a control signal comprising the movement position; and a control device (400) provided on the support body (100), electrically connected to the processing device (300), and configured to receive the control signal and control the support body (100) to move.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate a prediction for a state of an environment. One of the methods includes obtaining surfel data comprising a plurality of surfels, wherein each surfel corresponds to a respective different location in an environment, and each surfel has associated data that comprises an uncertainty measure; obtaining sensor data for one or more locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining one or more particular surfels corresponding to respective locations of the obtained sensor data; and combining the surfel data and the sensor data to generate a respective object prediction for each of the one or more locations of the obtained sensor data.

A controller for an unmanned aerial vehicle (UAV) comprising an image capture means, the controller comprising: inputs arranged to receive: positional data relating to the UAV, a vehicle and a user device; image data captured by the image capture means; a processor arranged to process the received positional data to determine the relative locations of the UAV, vehicle and user device; an output arranged to output a control signal for controlling the UAV and to output an image signal comprising captured image data; wherein the processor is arranged to: generate the control signal for the UAV such that the image data captured by the image capture means comprises at least an image of an obscured portion of the vehicle that is obscured from a field of view of a user of the user device.

Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.

In one embodiment, a method for extracting point cloud features for use in localizing an autonomous driving vehicle (ADV) includes selecting a first set of keypoints from an online point cloud, the online point cloud generated by a LiDAR device on the ADV for a predicted pose of the ADV; and extracting a first set of feature descriptors from the first set of keypoints using a feature learning neural network running on the ADV, The method further includes locating a second set of keypoints on a pre-built point cloud map, each keypoint of the second set of keypoints corresponding to a keypoint of the first set of keypoint; extracting a second set of feature descriptors from the pre-built point cloud map; and estimating a position and orientation of the ADV based on the first set of feature descriptors, the second set of feature descriptors, and a predicted pose of the ADV.

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 system and method for determining a traffic sign violation are provided. The method may include obtaining a traffic rule corresponding to the traffic sign. The method may further include acquiring, by at least one camera, video data associated with a scene around a traffic sign. The video data may include a series of frames. The method may further include identifying the vehicle in the series of frames and determining whether the vehicle violates the traffic rule based on the series of frames. In response to the determination that the vehicle violates the traffic rule, the method may further include obtaining information of the vehicle and transmitting the information of the vehicle to a server.

In one embodiment, a method includes determining an operational status of a vehicle including a radar antenna. The operational status is related to autonomous-driving operations of the vehicle in an environment. The method includes determining an expected amount of signaling resources associated with the radar antenna to be utilized by the vehicle while the vehicle performs the autonomous-driving operations, based at least on the operational status of the vehicle and the environment. The method includes determining to switch one or more of the signaling resources associated with the radar antenna from a first mode to a second mode based on the expected amount of signaling resources to be utilized by the radar antenna while the vehicle performs the autonomous-driving operations. The method includes causing the one or more of the signaling resources associated with the radar antenna to switch from the first mode to the second mode.