The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

A geographic area mapping system may enable collecting, from a set of mobile devices, radio frequency data, the radio frequency data comprising information about a set of network connections in the geographic area; collecting lidar data for the geographic area; generating a mapping between the collected radio frequency data and the collected lidar data for the geographic area; and providing a visualization of the mapped radio frequency data and lidar data for the geographic area.

Described herein are systems, methods, and non-transitory computer readable media for generating fused sensor data through metadata association. First sensor data captured by a first vehicle sensor and second sensor data captured by a second vehicle sensor are associated with first metadata and second metadata, respectively, to obtain labeled first sensor data and labeled second sensor data. A frame synchronization is performed between the first sensor data and the second sensor data to obtain a set of synchronized frames, where each synchronized frame includes a portion of the first sensor data and a corresponding portion of the second sensor data. For each frame in the set of synchronized frames, a metadata association algorithm is executed on the labeled first sensor data and the labeled second sensor data to generate fused sensor data that identifies associations between the first metadata and the second metadata.

A system includes a tracking system, a controller-circuit, and a device. The tracking system is configured to detect and track an object, and includes one or more of a computer vision system, a radar system, and a LIDAR system. The controller-circuit is disposed in a host vehicle, and is configured to receive detection signals from the tracking system, process the detection signals, determine, whether an object is detected based on the processed detecting signals, and in accordance with a determination that an object is detected, output command signals. The device is adapted to be mounted to the host vehicle, and is configured to receive the command signals and thereby provide a dynamic visual indication adapted to change in accordance with orientation changes between the host vehicle and the object. The dynamic visual indication is viewable from outside of the host vehicle.

Disclosed is an indoor SLAM method based on three-dimension (3D) lidar and ultra-wide band (UWB). Firstly, a UWB positioning system is deployed in an indoor area, then a robot carrying 3D lidar sensors explores the indoor area, and finally, a SLAM algorithm integrating lidar data and UWB data is used to generate a map of the explored area. The method specifically includes the following steps: determining a relative pose transformation between the 3D laser SLAM coordinate system and the UWB positioning coordinate system; using UWB data to provide initial value for inter-frame matching of laser odometer; using UWB data to add constraints to SLAM back-end pose graph; and performing loop detection based on curvature feature coding. This application breaks through the limitation of lighting conditions of indoor areas, eliminates the accumulated errors of SLAM, and constructs a high-quality indoor map.

Provided is an object recognition device including a temporary setting unit and an update processing unit. The temporary setting unit sets, based on specifications of an external information sensor that has detected an object, a position of at least one candidate point on the object. The update processing unit corrects a position of a detection point with respect to the external information sensor at a time when the external information sensor has detected the object based on the position of the candidate point on the object, and updates track data indicating a track of the object based on a position of the detection point with respect to the external information sensor after the correction.

A map construction method and a related apparatus are provided. The method includes: obtaining, based on manual driving track data and/or an obstacle grid map, road information, intersection information, and lane information of a region through which a vehicle has traveled; obtaining road traffic direction information based on the manual driving track data and the road information, and obtaining lane traffic direction information based on the lane information and the road traffic direction information; obtaining intersection entry and exit point information based on the intersection information and the lane traffic direction information; and performing, based on the intersection entry and exit point information, an operation of generating a virtual topology center line to obtain an autonomous driving map of the region through which the vehicle has traveled, where the virtual topology center line is a traveling boundary line of the vehicle in an intersection region.

Certain embodiments provide an imaging method for a non-line-of-sight object and an electronic device. In certain embodiments, the method includes: detecting a first input operation; and generating first image data in response to the first input operation. The first image data is imaging data of the non-line-of-sight object obtained by fusing second image data and third image data. The first image data includes position information between the non-line-of-sight object and a line-of-sight object. The second image data is imaging data of the line-of-sight object captured by the optical camera. The third image data is imaging data of the non-line-of-sight object captured by the electromagnetic sensor.

Included are an object identification unit that identifies an identified object in an image; a mapping unit that generates a superimposed image by superimposing target points corresponding to ranging points and superimposing a rectangle surrounding the identified object to the image; an identical-object determination unit that specifies, in the superimposed image, two target points closest to the left and right line segments of the rectangle inside the rectangle; a depth addition unit that specifies, in a space, the positions of two edge points indicating the left and right edges of the identified object based on two ranging points corresponding to the two specified target points, and calculates two depth positions of two predetermined corresponding points different from the two edge points; and an overhead-view generation unit that generates an overhead view of the identified object from the positions of the two edge points and the two depth positions.

A ground penetrating radar device and/or other sensor such as LIDAR, pressure, or temperature sensors is mounted on a mobile device, and is adapted, during motion of the mobile device, to sense characteristics of asphalt pavement on which the mobile device is moving, prior to compaction of the asphalt pavement by rollers. A processor, functionally associated with at least one sensor, receives from the sensor signals relating to characteristics of the asphalt pavement on which the mobile device is moving, and computes, based on the received signals, at least one compaction characteristic of the asphalt pavement. The processor provides a mapping of computed desired change in compaction characteristics to regions of the asphalt pavement during the rolling process. During rolling, at least one sensor measures the change in compaction and assesses when the change in compaction matches the desired optimal compaction based on the pre-generated map.