To implement a configuration to calculate a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a requested recovery ratio (RRR) for each road section, and issue a manual driving recovery request notification on the basis of the calculated time. A data processing unit is included, which calculates a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a predefined requested recovery ratio (RRR) from automatic driving to manual driving and determines notification timing of a manual driving recovery request notification on the basis of the calculated time. The data processing unit acquires the requested recovery ratio (RRR) for each road section set as ancillary information of a local dynamic map (LDM), and calculates the manual driving recoverable time for each road section scheduled to travel, using learning data for each driver.

A method of updating a road information, an electronic device, and a storage medium, which relate to an artificial intelligence technology field, in particular to fields of computer vision, deep learning, big data, high-definition map, intelligent transportation, automatic driving and autonomous parking, cloud service, Internet of Vehicles and intelligent cabin technologies. The method includes: processing image data corresponding to a target road region to obtain a set of first road lines; obtaining a set of second road lines according to a trajectory map corresponding to the target road region; calibrating the set of first road lines by using the set of second road lines to obtain a set of third road lines; combining the set of third road lines and a set of historical road lines corresponding to the target road region to obtain a combination result; and updating the set of historical road lines according to the combination result.

The present disclosure relates generally to identification of drivable surfaces in connection with autonomously performing various tasks at industrial work sites and, more particularly, to techniques for distinguishing drivable surfaces from non-drivable surfaces based on sensor data. A framework for the identification of drivable surfaces is provided for an autonomous machine to facilitate it to autonomously detect the presence of a drivable surface and to estimate, based on sensor data, attributes of the drivable surface such as road condition, road curvature, degree of inclination or declination, and the like. In certain embodiments, at least one camera image is processed to extract a set features from which surfaces and objects in a physical environment are identified, and to generate additional images for further processing. The additional images are combined with a 3D representation, derived from LIDAR or radar data, to generate an output representation indicating a drivable surface.

Provided herein is a method, apparatus, and computer program product for identifying locations along a road segment as a tunnel based on point cloud data. Methods may include: receiving point cloud data representative of an environment of a trajectory along a road segment; generating, from the point cloud data, one or more two-dimensional images in one or more corresponding planes orthogonal to the trajectory; determining, for the one or more two-dimensional images, a probability as to whether a respective two-dimensional image is captured within a tunnel along the road segment; and classifying a point along the road segment at a position corresponding to a respective one of the one or more two-dimensional images as a tunnel point in response to the probability as to whether the respective two-dimensional image is captured within a tunnel along the road segment satisfying a predetermined value.

The present disclosure provides a method comprising identifying at least one of a characteristic and an identity of an item for delivery from an origin to a destination; identifying a plurality of possible routes between the origin and the destination using mapping information, the mapping information including for each of the plurality of possible routes, a characterization of each of a plurality of route segments comprising the possible route; evaluating the plurality of possible routes in view of the identified at least one of the item characteristic and the item identity to select one of the plurality of possible routes; and providing the selected one of the plurality of possible routes to a vehicle, wherein the vehicle delivers the item from the origin to the destination via the identified route.

An apparatus for updating a map includes one or more processors configured to: receive feature data from a vehicle traveling on a predetermined road section for a feature in the road section related to travel of vehicles via a communication circuit, the feature data indicating the position of the feature, measure the accuracy of the position of the feature indicated by feature data obtained by the vehicle, based on the difference between the position of the feature indicated by the received feature data and a reference position of a corresponding feature, determine whether the accuracy satisfies a predetermined accuracy condition, and set contribution of the feature data received from the vehicle to update of map information indicating the position of the feature. The contribution is set lower when the accuracy does not satisfy the accuracy condition than when the accuracy satisfies the accuracy condition.

Described herein are methods of detecting and labeling features within an image of an environment. Methods may include: receiving sensor data from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region; detecting, using a perception module, at least one vehicle within the image of the geographic region; identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle; based on the identification of the area around the vehicle as a road segment, identifying features within the area as road features based on a context of the area; generating a map update for the road features of the road segment; and causing a map database to be updated with the road features of the road segment.

An example apparatus includes at least one processor and at least one memory coupled to the at least one processor and storing programming instructions for execution by the at least one processor to: obtain map data at a current moment, where the map data includes a first road area at the current moment, the special road condition is located in the first road area, the first road area is obtained based on a road condition model and a vehicle parameter within a preset time period before the current moment, and the road condition model represents a correspondence between a feature of the vehicle parameter and the special road condition; and determine, based on a planned route of a vehicle, that a second road area exists in the planned route of the vehicle, where the second road area is in the first road area.

A shielding detection unit (111) detects one or more shielded sections in which a road surface of a target road, on which a measuring vehicle has traveled, has not been measured, based on three-dimensional point group data indicating a three-dimensional coordinate value of each measurement spot that has been measured by a laser scanner mounted on the measuring vehicle. A result display unit (112) generates a target road map and displays the target road map, the target road map indicating the target road and indicating the one or more shielded sections in a manner to distinguish the one or more shielded sections from each unshielded section in which the road surface of the target road has been measured.

A road surface information providing apparatus includes an information acquisition unit, a road surface condition estimation unit, a risk map generation unit, and a transmission unit. The information acquisition unit acquires vehicle information from at least one vehicle with a wheel detected as being idling among vehicles that are traveling in a predetermined region, and acquire weather information about a surrounding area around the at least one vehicle. The predetermined region includes the surrounding area around the at least one vehicle. The road surface condition estimation unit generates road surface information about an estimated condition of a road surface in the predetermined region on the basis of the vehicle information and the weather information. The risk map generation unit generates a risk map in which the road surface information is correlated with map information. The transmission unit transmits the risk map to the vehicles traveling in the predetermined region.