Abstract: Provided are a lane line recognition method, an electronic device and a storage medium, relating to a technical field of artificial intelligence, in particular to technical fields of intelligent transportation, automatic driving and deep learning. The lane line recognition method includes: extracting a basic feature of an original image; recognizing at least one lane line node in the original image by using the basic feature of the original image; extracting a local feature from the basic feature of the original image by using the at least one lane line node; fusing the basic feature and the local feature; and recognizing a lane line in the original image based on a fused result.

Abstract: The present disclosure discloses a vehicle control method and apparatus, a device and a computer storage medium, and relates to the technical fields of autonomous driving and intelligent transportation. A specific implementation solution involves: determining vehicles in a preset geo-fencing region; determining a vehicle weight of each said vehicles according to a vehicle type and a waiting duration of each said vehicles; estimating, according to the vehicle weights of the vehicles in each of lanes in the geo-fencing region and positions of the vehicles in each said lanes, a duration to be waited in each said lanes; and generating a control instruction for each said vehicles according to the respective durations to be waited in each said lanes and the respective positions of the vehicles in each said lanes, the control instruction including a state instruction and/or a target speed instruction.

Abstract: The present disclosure provides a method and apparatus of estimating a road condition, and a method and apparatus of establishing a road condition estimation model, which relates to a field of big data and intelligent traffic. The method includes: acquiring, for a first preset duration before a first moment, a sequence of user tracks for a road and a sequence of road images for the road; extracting a track-related feature of the road from the sequence of the user tracks, and extracting an image-related feature of the road from the sequence of the road images; and inputting the track-related feature of the road and the image-related feature of the road into a pre-trained road condition estimation model, so as to determine, for a second preset duration after the first moment, a road condition information of the road by using an estimated result of the road condition estimation model.

Abstract: The present application discloses a map data updating method, an apparatus, a device and a readable storage medium. The specific implementation solution is: after receiving road information reported by an electronic device, a server obtains multiple sequences according to the road information, and each road information belonging to the same sequence has the same type and location. After that, the server inputs each road information contained in the sequences to a pre-trained neural network model, so that the neural network model outputs a recognition result according to the sequences. The server updates map data according to the recognition result. With such solution, valid road information is recognized by combining context of each road information in the sequences and the neural network technology, and the map data is updated, which achieves the purpose of accurately updating the map data.

Abstract: Provided are a road network extraction method, a device, and a storage medium, which relate to the technical field of artificial intelligence and, in particular, to the fields of image processing, computer vision, and the like and are specifically applicable to scenarios such as intelligent transportation and a smart city. A specific implementation scheme includes: extracting a first road network of a target region according to user trajectories of the target region; extracting a second road network of the target region according to a satellite aerial image of the target region; and extract a target road network of the target region according to the first road network, the second road network, and the user trajectories. Efficient and accurate road network extraction can be achieved through techniques in embodiments of the present disclosure.

Abstract: The present disclosure discloses a navigation method, a device, and a medium. The method includes: planning candidate routes based on a start position and an end position of a user, and obtaining a sequence of candidate road sections in each candidate route; matching each candidate road section in the sequence of candidate road sections in each candidate route with an association relationship between an actual road section and actual road auxiliary information, to obtain a sequence of auxiliary information of each candidate route; and sorting the auxiliary information in the sequence of auxiliary information of each candidate route to obtain a sorted result corresponding each candidate route, and selecting target road auxiliary information from the sequence of auxiliary information of each candidate route based on the sorted result, so as to select a target route from the candidate routes based on the target road auxiliary information.

Abstract: 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.

Abstract: A method of updating map data, an electronic device, and a storage medium, which relate to a field of computer technology, and in particular to intelligent transportation technology. The method of updating map data includes: acquiring a traffic event text, where the traffic event text includes location data and reference direction data; determining candidate road data in the map data based on the location data; determining target road data from the candidate road data based on the reference direction data; updating the target road data based on the traffic event text.

Abstract: A method for processing a map, an electronic device, and a storage medium, which relate to a technical field of computer technology, in particular to computer vision technology and high-definition map technology. The method includes: segmenting a first road line to obtain a plurality of first sub-road lines, wherein the first road line is obtained according to a segmentation mask for an image, and the image corresponds to a target region; segmenting a second road line to obtain a plurality of second sub-road lines, wherein the second road line is obtained according to a trajectory information corresponding to the target region; and determining a target road line according to first similarities between the plurality of first sub-road lines and the plurality of second sub-road lines.

Abstract: A method for processing behavior data, a method for controlling an autonomous vehicle, apparatuses thereof, a device, a storage medium, a computer program product, and an autonomous vehicle are provided. The method includes: acquiring historical driving data, the historical driving data comprising lane-level navigation data; and performing data mining on the historical driving data to obtain driving feature information, the driving feature information comprising at least one of: a lane-change position feature, a traveling speed feature, or a traveling path feature.

Abstract: The disclosure provides a method for determining an intersection missing traffic restriction information and an electronic device. The method includes: obtaining trajectory information corresponding to the intersection; determining a traffic anomaly occurring at the intersection based on the trajectory information; obtaining lane line information of a road section connected by the intersection; and determining the intersection missing traffic restriction information based on the lane line information.

Abstract: The present disclosure provides a method for integratedly updating map data, a device, and a storage medium, relates to the field of artificial intelligence technology such as vehicle-road coordination and intelligent transportation. An embodiment of the method includes: acquiring map update data; generating an updated confidence of map features based on the map update data; and updating uniformly a first-precision map and a second-precision map based on the updated confidence of the map features, where precision of the first-precision map is higher than precision of the second-precision map.

Abstract: The present disclosure discloses a method, apparatus and electronic device for early-warning, relates to the field of internet of vehicle and used for an intelligent traffic scenario. The specific implementations are: determining a target object crossing a road at a current moment, the road crossed by the target object being a first road; determining a target area based on a location of the target object; determining a target vehicle on the first road based on locations of vehicles on the first road as well as the target area; and sending an early-warning message to the target vehicle. It is using the locations of vehicles on the first road as well as the location of the target object crossing the road to determine the target vehicle during determination of the target vehicle among the vehicles on the first road, in order to send early-warning message to the target vehicle to achieve early-warning of the target vehicle and increase precision of the early-warning.

Abstract: A method of rectifying a text image, a training method, an electronic device, and a medium, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, deep learning technology, intelligent transportation and high-precision maps. An exemplary implementation includes: performing, based on a gating strategy, a plurality of first layer-wise processing on a text image to be rectified, so as to obtain respective feature maps of a plurality of layer levels, wherein each of the feature maps includes a text structural feature related to the text image to be rectified, and the gating strategy is configured to increase an attention to the text structural feature; and performing a plurality of second layer-wise processing on the respective feature maps of the plurality of layer levels, so as to obtain a rectified text image corresponding to the text image to be rectified.

Abstract: The present disclosure provides a method and an apparatus for monitoring road data, an electronic device and a storage medium, and relates to the field of intelligent transportation. A specific implementation solution involves: acquiring a feature expression of a road; predicting, according to the feature expression of the road and a pre-trained change prediction model, a probability that road data of the road will change; and collecting the road data of the road based on the probability that the road data will change.

Abstract: The present disclosure provides a method and apparatus for processing an image. A specific implementation includes: acquiring a top view of a road; identifying a position of a lane line from the top view; cutting the top view into at least two areas, and determining, according to the position of the lane line in each area, a width of a lane in the each area and an average width of the lane in the top view; calculating a first perspective correction matrix by optimizing a first loss function, the first loss function being used to represent a difference between the width of the lane in the each area and the average width of the lane in the top view; and performing a lateral correction on the top view through the first perspective correction matrix to obtain a first corrected image.

Abstract: The present disclosure provides a method and apparatus for automatically producing map data. The method includes: performing track rectification on crowdsourcing tracks based on corresponding standard tracks, and locating each map element included, based on depth information of track point images included in the rectified crowdsourcing tracks; comparing a latest map element obtained based on the rectified crowdsourcing tracks locating and an old map element at a corresponding locating position using a pre-built entity semantic map; determining, in response to a change in the latest map element compared to the old map element, a target processing method according to a processing standard of a changed map element pre-abstracted from a map element update specification; and processing the latest map element according to the target processing method to obtain a processed latest map.

Abstract: A method and an apparatus for visual positioning based on mobile edge computing are provided. In the method, the mobile edge computing node receives an environment image captured by a to-be-positioned device in an area covered by the mobile edge computing node; the mobile edge computing node determines a target image matching the environment image from multiple candidate images, and calculates the position and pose information of the to-be-positioned device based on the position and pose information of the device for capturing the target image; and the mobile edge computing node sends the position and pose information of the to-be-positioned device to the to-be-positioned device, so that the to-be-positioned device determines the positioning information in an electronic map according to the position and pose information.

Abstract: The present application discloses a method and apparatus for identifying an updated road, a device and a computer storage medium, and relates to the field of big data technologies. A specific implementation solution is as follows: comparing a road area extracted based on the latest satellite image with a road area extracted based on a historical satellite image, to obtain a candidate updated road; mapping the candidate updated road into road network data according to a coordinate position of the candidate updated road; acquiring a user trajectory set corresponding to the candidate updated road within a recent preset period; and identifying, based on a matching result between the user trajectory set and the road network data, whether the candidate updated road is an actual updated road. Updated roads can be more accurately identified through the method according to the present application.

Abstract: The present disclosure provides a method and apparatus of estimating a road condition, and a method and apparatus of establishing a road condition estimation model, which relates to a field of big data and intelligent traffic. The method includes: acquiring, for a first preset duration before a first moment, a sequence of user tracks for a road and a sequence of road images for the road; extracting a track-related feature of the road from the sequence of the user tracks, and extracting an image-related feature of the road from the sequence of the road images; and inputting the track-related feature of the road and the image-related feature of the road into a pre-trained road condition estimation model, so as to determine, for a second preset duration after the first moment, a road condition information of the road by using an estimated result of the road condition estimation model.