A navigation system for a host vehicle is provided. The system may comprise at least one processing device comprising circuitry and a memory. The memory includes instructions that when executed by the circuitry cause the at least one processing device to: receive a plurality of images acquired by a camera, the plurality of images being representative of an environment of the host vehicle; analyze the plurality of images to identify a presence in the environment of the host vehicle a navigation rule suspension condition; temporarily suspend at least one navigational rule in response to identification of the navigation rule suspension condition; and cause at least one navigational change of the host vehicle unconstrained by the temporarily suspended at least one navigational rule.

A method for controlling a vehicle is provided. The vehicle includes an image capturing device. The method includes: controlling the image capturing device to collect an image of a scene where the vehicle is located; acquiring projection information of a projection pattern in the image; determining image-altering information corresponding to the projection pattern according to the projection information; and controlling movement of the vehicle according to the image-altering information.

Methods, apparatus, electronic devices, and computer-readable storage media for calibrating image acquiring devices are provided. In one aspect, a computer-implemented method includes: obtaining a scene image of a preset scene acquired by an image acquiring device disposed on a traveling device, the preset scene including at least two parallel lines, the traveling device being located between adjacent two parallel lines, and sides of the traveling device being substantially parallel to the two parallel lines; based on the scene image, determining two line segments, and first coordinate information of a plurality of target reference points on each line segment of the two line segments in a pixel coordinate system and second coordinate information of the plurality of target reference points in a world coordinate system; and determining a homography matrix corresponding to the image acquiring device based on the first coordinate information and the second coordinate information.

A processor-implemented method with calibration includes: detecting a preset pattern comprised in a surface of a road from a driving image of a vehicle; transforming image coordinates in an image domain of the pattern into world coordinates in a world domain; determining whether to calibrate a camera capturing the driving image by comparing a size predicted based on the world coordinates of the pattern and a reference size of the pattern; in response to a determination to calibrate the camera, determining relative world coordinates of the pattern with respect to a motion of the camera, using images captured by the camera at different time points; transforming the relative world coordinates of the pattern into absolute world coordinates of the pattern; and calibrating the camera using a corresponding relationship between the absolute world coordinates of the pattern and the image coordinates of the pattern.

The present application discloses a method, an apparatus, a system, and a storage medium for calibrating an exterior parameter of an on-board camera, relating to the field of autonomous driving technologies. A specific implementation scheme of the method in the application is: preprocessing two frames of images of a former frame and a latter frame collected by the on-board camera; performing feature point matching on the two preprocessed frames of images to obtain matched feature points; determining a moving posture of the on-board camera according to the matched feature points; determining a conversion relationship between a vehicle coordinate system and an on-board camera coordinate system according to the moving posture, and obtaining the external parameter of the on-board camera relative to a vehicle body.

System, methods, and other embodiments described herein relate to lane keeping in a vehicle. In one embodiment, a method includes determining lane boundaries according to at least the sensor data and a map. The method includes defining a reference system over a lane defined by the lane boundaries. The method includes evaluating vehicle boundary points within the reference system as a cost in optimizing a trajectory of the vehicle and using an R-function that defines geometric relationships between the vehicle boundary points and the reference system. The method includes providing an indicator about the trajectory to control the vehicle.

An imaging system to be mounted in a vehicle is provided. The imaging system includes an optical wide-angle camera acquiring an image, and an object identifying apparatus. This object identifying apparatus executes a distortion correcting process with the acquired image and apply previously-prepared reference patterns to the image which has been subjected to the distortion correcting process such that objects in the acquired image are recognized.

A system may automatically create training datasets for training a segmentation model to recognize features such as lanes on a road. The system may receive sensor data representative of a portion of an environment and map data from a map data store including existing map data for the portion of the environment that includes features present in that portion of the environment. The system may project or overlay the features onto the sensor data to create training datasets for training the segmentation model, which may be a neural network. The training datasets may be communicated to the segmentation model to train the segmentation model to segment data associated with similar features present in different sensor data. The trained segmentation model may be used to update the map data store, and may be used to segment sensor data obtained from other portions of the environment, such as portions not previously mapped.

Methods, systems, apparatuses, and computer program products are provided that are configured to perform localization of position data, specifically using a trained localization neural network. In the context of an apparatus, the apparatus is caused to receive observed feature representation data. The apparatus is further configured to transform the observed feature representation data into standardized feature representation data utilizing a trained localization neural network. The apparatus is further configured to compare the standardized feature representation data and the map feature representation data and identify local position data based on the comparison.

A system for inferring lane boundaries via vehicle telemetry for a road section is provided. The system includes a computerized remote server device operable to receive through a communications network sensor data describing lane markings upon roads bordering the road section, determine established lanes upon roads bordering the road section based upon the sensor data, receive through the communications network the vehicle telemetry generated by a plurality of vehicles traversing the road section, generate inferred lanes for the road section based upon the vehicle telemetry, match the inferred lanes to the established lanes to generate unified lane geometries, and publish the unified lane geometries.