Systems and methods are provided for determining a road profile along a predicted path. In one implementation, a system includes at least one image capture device configured to acquire a plurality of images of an area in a vicinity of a user vehicle; a data interface; and at least one processing device configured to receive the plurality of images captured by the image capture device through the data interface; and compute a profile of a road along one or more predicted paths of the user vehicle. At least one of the one or more predicted paths is predicted based on image data.

In one aspect, a vehicle localization system implements the following steps: receiving a predetermined road map; receiving at least one captured image from an image capture device of a vehicle; processing, by a road detection component, the at least one captured image, to identify therein road structure for matching with corresponding structure of the predetermined road map, and determine a location of the vehicle relative to the identified road structure; and using the determined location of the vehicle relative to the identified road structure to determine a location of the vehicle on the road map, by matching the road structure identified in the at least one captured image with the corresponding road structure of the predetermined road map.

A driving assistance device includes a guide line detecting unit, a remaining distance acquiring unit, and a braking control unit. The guide line includes a base-point mark which is provided at a position which is separated a first distance from the scheduled stop position. The guide line detecting unit detects the base-point mark at a measurement position of a captured image and sets the detected position of the base-point mark at the measurement position of the captured image as the center of the base-point mark in an extending direction of the guide line when the base-point mark is detected at the measurement position of the captured image. The remaining distance acquiring unit acquires the remaining distance on the basis of the position of the base-point mark set by the guide line detecting unit.

In accordance with an aspect of the present disclosure, there is provided a method of calibrating a camera for a vehicle, comprising: obtaining attitude angle information of the vehicle by using a traveling direction of the vehicle obtained based on a satellite signal, and a vertical direction from ground obtained based on a high definition map; obtaining attitude angle information of the camera mounted on the vehicle by matching an image captured by the camera to the high definition map; and obtaining coordinate system transformation information between the vehicle and the camera by using the attitude angle information of the vehicle and the attitude angle information of the camera.

A navigation system may include a processor programmed to receive, from a camera of the host vehicle, one or more images captured from an environment of the host vehicle, and analyze the one or more images to detect an indicator of an intersection. The processor may also be programmed to determine, based on output received from at least one sensor of the host vehicle, a stopping location of the host vehicle relative to the detected intersection, and analyze the one or more images to determine an indicator of whether one or more other vehicles are in front of the host vehicle. The processor may further be programmed to send the stopping location of the host vehicle and the indicator of whether one or more other vehicles are in front of the host vehicle to a server for use in updating a road navigation model.

Systems, methods, and non-transitory computer-readable media can receive a geometric map and a semantic map associated with a geographic area, the semantic map comprising semantic data associated with vehicle navigation. A first semantic position estimate associated with a first piece of semantic data contained in the semantic map is generated based on semantic data location information associated with the first piece of semantic data. A final position for the first semantic position estimate is received. One or more three-dimensional semantic labels are applied to the geometric map based on the final position of the first semantic position estimate. A warped semantic map is generated. Generating the warped semantic map comprises warping the semantic map based on the one or more three-dimensional semantic labels.

The present disclosure relates to a method, a device, and a storage medium for vehicle intelligent driving control. The vehicle intelligent driving control method comprises: collecting, by means of a vehicle-mounted camera of a vehicle, a video stream of a road image of a scene where the vehicle is; detecting a target object in the road image to obtain a bounding box of the target object; determining, in the road image, a free space for the vehicle; adjusting the bounding box of the target object according to the free space; and carrying out intelligent driving control on the vehicle according to the adjusted bounding box. The bounding box of the target object can be used to identify the position and determine the actual position of the target object more precisely, such that intelligent driving control can be carried out on the vehicle more accurately.

A system and method for localizing an entity includes a processing system with at least one processing device. The processing system is configured to obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors. The processing system is configured to produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors. The processing system is configured to extract sets of localization features from the sensor data of the second set of sensors. The processing system is configured to generate visualization images in which each visualization image includes a respective set of localization features. The processing system is configured to generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images.

A display system includes: a LiDAR measuring a position of a first surrounding vehicle; a position measuring device measuring a position of a second surrounding vehicle not measured in position by the LiDAR; a display device displaying dividing line icons corresponding to dividing lines around the ego vehicle and surrounding vehicle icons corresponding to the surrounding vehicles around the ego vehicle, and a controller controlling display of the dividing line icons and the surrounding vehicle icons at the display device. The controller is configured to specify a display position in a front-back direction corresponding to a position in the front-back direction of the second surrounding vehicle measured by the position measuring device and a display position in a left-right direction corresponding to a position in the left-right direction in a lane in which the second surrounding vehicle is running, as a display position of a surrounding vehicle icon.

Systems and methods for validating drive pose refinement are provided. In some aspects, a method includes receiving image data that depicts an area of interest, and receiving a plurality of virtual points generated using the image data. The method also includes selecting at least one drive in the area of interest that captures the plurality of virtual points, and generating a refined pose track for each of the at least one drive by applying a drive alignment process to drive data from the at least one drive using the plurality virtual points. The method further includes comparing the refined pose track to a control pose track generated using control repoints, and generating, based on the comparison, a report that validates the refined pose track.