The present disclosure is related to generating map data explicitly indicating a total drivable surface, which may include multiple types of drivable surfaces. For instance, a given portion of a map may include map data indicating a combination of various drivable surfaces, such as road segments, lane properties, intersections, parking areas, shoulders, driveways, etc. Examples of the present disclosure join these different types of drivable surfaces into combined map data that explicitly indicates a total drivable surface, such as a perimeter boundary indicating or representing a transition from a drivable surface to a non-drivable surface. The map data indicating the total drivable surface may be searched to determine information related to a drivable surface boundary, such as location and type. This boundary information may be used in various contexts, such as when planning a trajectory or remotely controlling a vehicle.

A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.

In one embodiment, a system/method generates a driving trajectory for an autonomous driving vehicle (ADV). The system perceives an environment of an autonomous driving vehicle (ADV). The system determines one or more bounding conditions based on the perceived environment. The system generates a first trajectory using a neural network model, wherein the neural network model is trained to generate a driving trajectory. The system evaluates/determines if the first trajectory satisfies the one or more bounding conditions. If the first trajectory satisfies the one or more bounding conditions, the system controls the ADV autonomously according to the first trajectory. Otherwise, the system controls the ADV autonomously according to a second trajectory, where the second trajectory is generated based on an objective function, where the objective function is determined based on at least the one or more bounding conditions.

A method includes obtaining or holding available first radio map information representing a first radio map for a first environment. The method also includes determining, at least partially based on said first radio map information, second radio map information representing a second radio map for a second environment. The second radio map contains or represents a respective radio coverage model for each radio device of a group of radio devices. A portion of the second environment at least partially covers the first environment. A density of radio coverage models contained in or represented by said second radio map in the portion of said second environment and at least partially covering the first environment is higher than a density of radio coverage models contained in or represented by the second radio map in a remaining portion of the second environment. A corresponding apparatus and computer program product are also provided.

A three-dimensional data creation method in a client device includes: creating three-dimensional data of a surrounding area of the client device using sensor information that is obtained through a sensor equipped in the client device and indicates a surrounding condition of the client device; estimating a self-location of the client device using the three-dimensional data created; and transmitting the sensor information obtained to a server or an other client device.

The present application discloses a verification method and device for a modeling route, an unmanned vehicle, and a storage medium, which relate to the technical field of computer vision and intelligent transportation. A specific implementation of the method in the present application lies in: acquiring a filtering threshold of a target road section, where the filtering threshold is related to image feature points corresponding to the target road section; verifying a modeling route corresponding to the target road section through the filtering threshold to obtain a verification result. According to the present application, availability of the modeling route can be directly verified with the filtering threshold while there is no need to verify the modeling route through manual driving of the vehicle, thereby effectively increasing the verification efficiency, protecting the vehicle from travelling along an unavailable modeling route and improving the driving experience.

A three-dimensional data creation method in a client device includes: creating three-dimensional data of a surrounding area of the client device using sensor information that is obtained through a sensor equipped in the client device and indicates a surrounding condition of the client device; estimating a self-location of the client device using the three-dimensional data created; and transmitting the sensor information obtained to a server or an other client device.

Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

A computing device receives data regarding a destination, a current GPS location, map data of the current location and destination, and a direction and angle of view of an augmented reality display device. The computing device accesses social media content related to an area that includes the location and the destination, and analyzes the content to determine recent conditions and attributes associated with the location and destination area. The computing device determines a path from the location to the destination based on the map data, the current location and destination, and the social media content. The computing device sends information to display a virtual image of the path overlaid on a real-world view of the augmented reality display device, based on the direction of view and the incline level of view, of the augmented reality display device.

The present disclosure relates to map update methods and apparatuses. In one example method, a terminal side device receives sensing information from a sensor, where the sensing information indicates data collected by the sensor. The terminal side device receives second information from a cloud device, where the second information comprises second indication information, and the second indication information indicates a first change of a map element on a map. The terminal side device sends first information to the cloud device based on the sensing information and the second information, where the first information comprises first indication information, and the first indication information indicates a second change of the map element on the map.