Abstract: A map matching method, an apparatus and an electronic device related to the technical field of artificial intelligence such as high-precision map, autonomous driving, internet of vehicles, and smart cockpit, where a specific implementation is: when matching a high-precision map with a standard-precision map, firstly acquire road network data corresponding to the high-precision map and the standard-precision map to be matched; and construct a first graph network based on the road network data corresponding to the high-precision map, and construct a second graph network based on the road network data corresponding to the standard-precision map, then determine a matching result between the high-precision map and the standard-precision map based on the first graph network and the second graph network. By the method using the graph network, the matching of the high-precision map with the standard-precision map can be accurately realized, thereby effectively improving accuracy of the matching result.

Abstract: The present disclosure provides a method for optimizing map data, a device and a storage medium, and in particular relates to artificial intelligence, intelligent transportation, smart cities and smart cockpits. A specific implementation is: performing a completeness detection of a lane line on received local map data comprising road condition information to obtain a detection result, the detection result comprising the lane line being complete or is the lane line having a missing area; in a case where the detection result is the lane line having the missing area, performing completion processing on the lane line having the missing area to obtain completed local map data; and performing a rationality detection on the completed local map data, and in a case of passing the detection, synthesizing the completed local map data with global map data to obtain an optimization result of map data.

Abstract: A map matching method, an apparatus and an electronic device related to the technical field of artificial intelligence such as high-precision map, autonomous driving, internet of vehicles, and smart cockpit, where a specific implementation is: when matching a high-precision map with a standard-precision map, firstly acquire road network data corresponding to the high-precision map and the standard-precision map to be matched; and construct a first graph network based on the road network data corresponding to the high-precision map, and construct a second graph network based on the road network data corresponding to the standard-precision map, then determine a matching result between the high-precision map and the standard-precision map based on the first graph network and the second graph network. By the method using the graph network, the matching of the high-precision map with the standard-precision map can be accurately realized, thereby effectively improving accuracy of the matching result.

Abstract: The disclosure provides a 3-dimensional reconstruction method, an electronic device and a storage medium. The method includes: obtaining an image sequence pre-collected by a camera; obtaining nominal intrinsic parameters of the camera, and preset initial values of distortion coefficients of the camera, the camera being a camera for collecting the image sequence; calculating fluctuation ranges of the distortion coefficients; performing the 3D reconstruction on the image sequence based on the nominal intrinsic parameters, the initial values of the distortion coefficients, and the fluctuation ranges of the distortion coefficients; and during the 3D reconstruction, optimizing an obtained visual point cloud and a camera pose.

Abstract: The present application discloses a road event detection method, an apparatus, a device and a storage medium, and relates to artificial intelligence, big data, intelligent transportation, automatic driving, and cloud computing technologies in data processing. A specific implementation is that: by obtaining road images collected and returned by multiple vehicles within a historical period, and performing image processing on single-frame road images to determine road events included in the road images, types and/or locations of the road events can be located accurately; further, by performing clustering on road event data of multiple frames of road images including the road events to obtain all road events occurring within the historical period and road event information, all road events occurring within the historical period can be determined accurately, and the types and/or the locations of the road events can be determined accurately, thereby improving the accuracy of road event detection.

Abstract: The disclosure provides a 3-dimensional reconstruction method, an electronic device and a storage medium. The method includes: obtaining an image sequence pre-collected by a camera; obtaining nominal intrinsic parameters of the camera, and preset initial values of distortion coefficients of the camera, the camera being a camera for collecting the image sequence; calculating fluctuation ranges of the distortion coefficients; performing the 3D reconstruction on the image sequence based on the nominal intrinsic parameters, the initial values of the distortion coefficients, and the fluctuation ranges of the distortion coefficients; and during the 3D reconstruction, optimizing an obtained visual point cloud and a camera pose.