A method and apparatus for detecting an obstacle, an electronic device, and a storage medium. A specific implementation of the method includes: detecting, by a millimeter-wave radar, position points of candidate obstacles in front of a vehicle; detecting, by a camera, a left road boundary line and a right road boundary line of a road on which a vehicle is located; separating the position points of the candidate obstacles according to the left road boundary line and the right road boundary line of the road on which the vehicle is located, and extracting position points between the left road boundary line and the right road boundary line; projecting the position points between the left road boundary line and the right road boundary line onto an image; and detecting, based on projection points of the position points on the image, a target obstacle in front of the vehicle.

A geographic object recognition unit (120) recognizes, using image data (192) obtained by photographing in a measurement region where a geographic object exists, a type of the geographic object from an image that the image data (192) represents. A position specification unit (130) specifies, using three-dimensional point cloud data (191) indicating a three-dimensional coordinate value of each of a plurality of points in the measurement region, a position of the geographic object.

Systems and methods for road typology scene annotation are provided. A method for road typology scene annotation includes receiving an image having a road scene. The image is received from an imaging device. The method populates, using a machine learning model, a set of attribute settings with values representing the road scene. An annotation interface is implemented and configured to adjust values of the attribute settings to correspond with the road scene. Based on the values of the attribute settings, a simulated overhead view of the respective road scene is generated.

Example localization systems and methods are described. In one implementation, a method receives a camera image from a vehicle camera and cleans the camera image using a VAE-GAN (variational autoencoder combined with a generative adversarial network) algorithm. The method further receives a vector map related to an area proximate the vehicle and generates a synthetic image based on the vector map. The method then localizes the vehicle based on the cleaned camera image and the synthetic image.

Systems, methods, and non-transitory computer readable media can perform operations comprising generating a first type of pose and a second type of pose based on visual geometry localization; determining a mode for planning a planning path for a vehicle based on at least one of the first type of pose and the second type of pose; and generating the planning path for the vehicle based on the mode.

A road deterioration diagnostic device according to the present disclosure includes: an image information acquisition unit which acquires an image in which a road has been photographed; a puddle detection unit which detects puddles on the basis of the acquired image; and a road deterioration detection unit which detects road deterioration on the basis of the shape of the detected puddles.

A method and a device for identifying road markings, and a monocular camera is provided. The method includes: acquiring an original image captured by a monocular camera on a vehicle; determining a middle region where an object is located and left and right regions on both sides of the middle region from the original image; acquiring an image intensity distribution along a vertical direction of the original image in the left region, the right region and the middle region; and determining whether the object is a road marking by comparing an image intensity of the middle region relative to image intensities of the left region or the right region. The present application can achieve the object of accurately identifying road markings.

To provide an own position estimation apparatus and an own position estimation method which can correct the position coordinate of own vehicle, even if a periphery monitoring apparatus in which detection points detected with good accuracy at the same timing is few is used. An own position estimation apparatus detects relative positions of a road side wall based on detection information of a periphery monitoring apparatus; converts the past relative positions, into relative positions on a basis of the current position of the own vehicle, and superimposes the current relative positions and the past relative positions after conversion; searches for a relative position relation that a coincidence degree between the relative positions of the road side wall after superposition and the positions of the road side wall of the map data becomes high; and corrects the position coordinate of the own vehicle based on the relative position relation.

Disclosed are methods, devices, and computer-readable media for detecting lanes and objects in image frames of a monocular camera. In one embodiment, a method is disclosed comprising receiving a plurality of images; identifying a horizon in the plurality of images by inputting the plurality of images into a deep learning (DL) model (either stored on a local device or via a network call); determining one or more camera parameters based on the horizon; and storing or using the camera parameters to initialize a camera.

The disclosure provides a method for operating an autonomous vehicle. To operate the autonomous vehicle, a plurality of lane segments that are in an environment of the autonomous vehicle is determined and a first object and a second object in the environment are detected. A first position for the first object is determined in relation to the plurality of lane segments, and particular lane segments that are occluded by the first object are determined using the first position. According to the occluded lane segments, a reaction time is determined for the second object and a driving instruction for the autonomous vehicle is determined according to the reaction time. The autonomous vehicle is then operated based on the driving instruction.