A system, method, and computer-readable storage medium are disclosed that execute machine vision operations to categorize a locality. At least one embodiment accesses a map image of a locality, where the map image includes geographical artefacts corresponding to entities within the locality; analyzes the map image to detect the entities in the locality using the geographical artefacts; assigns entity classes to detected entities in the locality; assigns a locality score to the locality based on entity classes included in the locality; retrieves street view images for one or more of the detected entities in the locality; and analyzes street view images of the detected entities to assign one or more further classifications to the detected entities. Other embodiments include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

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 computer implemented method for determining the position of a vehicle, wherein the method comprises: determining at least one scan comprising a plurality of detection points, wherein each detection point is evaluated from a signal received at the at least one sensor and representing a location in the vehicle environment; determining, from a database, a predefined map, wherein the map comprises a plurality of elements in a map environment, each of the elements representing a respective one of a plurality of static landmarks in the vehicle environment, and the map environment representing the vehicle environment; matching the plurality of detection points and the plurality of elements of the map; determining the position of the vehicle based on the matching; wherein the predefined map further comprises a spatial assignment of a plurality of parts of the map environment to the plurality of elements, and wherein the spatial assignment is used for the matching.

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.

This disclosure discloses a vehicle scheduling method, apparatus and system, and relates to the field of scheduling. The method includes acquiring image information of each area; determining vehicle density information in each area according to the image information; configuring a first path cost corresponding to the path of each area according to the vehicle density information in each area; calculating a path value corresponding to each planning path among a plurality of planning paths from the starting position to at least one target position for a target vehicle, according to the first path cost configured for the path of each area; and determining an optimal planning path of the target vehicle by taking a minimum path value as a target.

The disclosure provides a method for managing a cleaning route in a smart city, an Internet of Things system and storage medium. The method includes: obtaining target information of a target area within a preset time period based on an object platform; sending the target information to a management platform through a sensor network platform; determining a cleaning route of the target area by processing the target information of the target area based on the management platform, including: determining an estimated amount of fallen leaves of each section of road in the target area; determining an estimated falling range of fallen leaves of each section of road in the target area; determining a cleaning difficulty evaluation value of each section of road based on the estimated amount of fallen leaves and the estimated falling range; and determining a cleaning route based on the cleaning difficulty evaluation value.

A navigation system includes a processing unit connected to a storage device for fetching and executing a navigation program to: determine a planned route; obtain a real-time image and real-time positioning data indicating a current location of the system; perform image recognition on the image, and determine whether a specific object exists in the image; determine whether a route adjustment condition is met based on the planned route, the positioning data and result of the determination; and adjust the planned route when the route adjustment condition is met. The route adjustment condition includes that a meaning conveyed by the specific object is in conflict with the planned route.

A navigation system adapted to be installed on a vehicle includes an output device and a processing device. The processing device executes the following steps after determining a planned route: obtaining real-time image data and performing image recognition on the image data to identify at least one lane from the image data; obtaining a real-time positioning result that indicates a current location of the navigation system; based on the planned route and the positioning result, selecting a target lane that corresponds to a moving directive presented by the planned route; and controlling the output device to visually output a route indicator that indicates the target lane.

Disclosed is an AR image provision method of a vehicle navigation system, including transmitting, by a processor, information on a guide point or vehicle speed information to an external server, searching, by an external server, for candidate vehicles based on the information on the guide point or the vehicle speed information, selecting, by the external server, an image providing vehicle from among the candidate vehicles according to priority set by the processor, generating, by the external server, a graphical object to be displayed in a camera image received from the image providing vehicle using augmented reality (AR), and displaying, by the processor, AR image information on the guide point including the camera image and the graphical object on a navigation screen.

A method and apparatus for positioning a vehicle. The method may include: acquiring an identification element and non-identification elements of a current vehicle. The method can further include matching the identification element of the current vehicle with position elements in a high-precision map to determine an initial position of the current vehicle. The method can further include using at least one position element in the high-precision map to perform observational constraints on the non-identification elements of the current vehicle to acquire position elements corresponding to the non-identification elements of the current vehicle. The method can further include adjusting the initial position of the current vehicle using the position elements corresponding to the non-identification elements of the current vehicle to obtain a target position of the current vehicle.