Abstract: Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N<M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

Abstract: Embodiments of a method, apparatus, device and computer readable storage medium for classifying driving scenario data includes: acquiring a first driving scenario data set from a crowdsourcing platform, driving scenario data in the first driving scenario data set having been classified; generating a driving scenario classification model at least based on the first driving scenario data set, for classifying driving scenario data collected by a collection entity; acquiring a rule for classifying the driving scenario data, the rule is generated based on a result of classifying the driving scenario data collected by the collection entity using the driving scenario classification model; updating the driving scenario classification model at least based on the rule.

Abstract: A method and an apparatus for generating a high precision map, and a storage medium for generating a high precision map. The method includes: performing a point cloud splicing process on target point cloud data to obtain a lidar pose corresponding to the target point cloud data; projecting the target point cloud data into a preset two-dimensional area based on the lidar pose to generate a map based on a reflection value and a height value; performing a self-positioning verification on the map based on the reflection value and the height value using the target point cloud data; and integrating, if a result of the self-positioning verification satisfies a preset condition, the map based on the reflection value and the height value into a reference map to generate the high precision map.

Abstract: The present disclosure provides a method and device for predicting a motion track of an obstacle and an autonomous vehicle, and relates to the technical field of autonomous driving, so as to at least solve the technical problem of low prediction precision of a motion track of an obstacle in an interaction scene. A specific implementation solution includes: environment information in a target scene, historical state information of a target obstacle and track planning information of a target vehicle are obtained, and the target obstacle is a potential interaction object of the target vehicle; and a motion track of the target obstacle is predicted based on the environment information, the historical state information and the track planning information.

Abstract: The disclosure provides a method for detecting an obstacle, an electronic device, and a storage medium, relates to the field of artificial intelligence, and in particular, to the field of automatic driving. A specific implementation solution includes: acquiring point cloud data collected by a lidar sensor during detection of an obstacle; converting the point cloud data into an obstacle feature map, where the obstacle feature map includes a to-be-detected image feature, and the to-be-detected image feature includes an obstacle feature; performing feature extraction on the to-be-detected image feature in the obstacle feature map to obtain the obstacle feature, increasing a weight value in the obstacle feature map of the obstacle feature during the feature extraction, and monitoring the feature extraction based on monitoring data generated by point cloud distribution data corresponding to the point cloud data; and determining a target obstacle according to the obstacle feature.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for positioning an autonomous vehicle. The method includes: matching a current point cloud projected image of a first resolution with a map of the first resolution to generate a first histogram filter based on the matching result; determining at least two first response areas in the first histogram filter based on a probability value of an element in the first histogram filter; generating a second histogram filter based on a result of matching a current point cloud projected image of a second resolution with a map of the second resolution and the at least two first response areas, the first resolution being less than the second resolution; and calculating a weighted average of probability values of target elements in the second histogram filter to determine a positioning result of the autonomous vehicle in the map of the second resolution.

Abstract: A method for determining parameters of cross sections of a single-hole four-lane highway tunnel includes the steps of determining surrounding rock pressure, constructing numerical models under different flatness ratios, calculating interval force of lining structures, calculating safety coefficients of the lining structures, performing contrastive analysis on the cross sections under different flatness ratios, obtaining reasonable cross section forms, and so on. This method provides a basis to formulate the specifications and standards for design of a single-hole four-lane highway tunnel and may provide a reference basis to design a cross section of a single-hole four-lane highway tunnel in new construction or reconstruction and expansion, thereby ensuring safety and economic efficiency of the cross section forms of the single-hole four-lane highway tunnel.

Abstract: A stationary object detecting method, a stationary object apparatus, and an electronic device are disclosed in embodiments of the present disclosure, the method includes: obtaining point cloud data of a scene and feature information of each of data points in the point cloud data; performing a triangulation network connection on each of the data points in the point cloud data to generate a triangular network model, and taking a picture of the triangular network model using a preset camera to obtain an image; obtaining a first data point corresponding to each pixel point in the image, and obtaining a feature map of the point cloud data according to feature information of each first data point; inputting the feature map into a classification model to obtain data points corresponding to the stationary object in the point cloud data.

Abstract: A method and an apparatus for extracting a lane line, a device, a computer readable-storage medium and a collection entity are provided. The method includes: obtaining a first group of lane lines of a road based on a first image generated from a point cloud collected by a laser radar; obtaining a second group of lane lines of the road based on a second image collected by a camera; and determining a lane line set of the road based on the first group of lane lines and the second group of lane lines.

Abstract: Embodiments of the present disclosure provide a method and a device for acquiring a map, a device, and a computer readable storage medium. The method includes: acquiring a reference map of an acquisition area; dividing the acquisition area into a plurality of sub-areas based on the reference map; generating a plurality of acquisition tasks corresponding to the plurality of sub-areas, wherein each of the plurality of acquisition tasks is configured to acquire a map of each of the plurality of sub-areas; and assigning each of the plurality of acquisition tasks to an acquisition entity, to enable the acquisition entity to acquire the map of each of the plurality of sub-areas.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N<M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

Abstract: Embodiments of the present disclosure generally relate to a method for controlling a simulation vehicle, an electronic device, and a computer readable storage medium. According to the method, a simulation vehicle determines a first region corresponding to a first simulation position where the simulation vehicle is located at a first time point; obtains first environment information about the first region and second environment information about a second region adjacent to the first region; sends the first environment information and the second environment information to a control device of the simulation vehicle; and determines a first action performed by the simulation vehicle at a second time point after the first time point based on a control signal in response to receiving the control signal from the control device, the control signal being determined by the control device based on the first environment information and the second environment information.

Abstract: Provided are a method and an apparatus for acquiring a control amount for a performance test of an unmanned vehicle, including: acquiring coordinates of y key points on a trajectory of a simulated obstacle vehicle and speeds at the y key points, where y?2; acquiring trajectory equations of the obstacle vehicle between every two adjacent key points according to the coordinates of the y key points, where the trajectory equation is an nth degree polynomial equation, n?2; acquiring accelerations of the obstacle vehicle between every two adjacent key points according to speeds at the y key points and a trajectory curve corresponding to the trajectory equation; for each running duration, according to the accelerations, acquiring a dynamic attribute and a coordinate when the obstacle vehicle has run for the running duration according to trajectories corresponding to the trajectory equations.

Abstract: The present application discloses to a method for detecting map quality, an electronic device and a storage medium, and relates to the automatic driving field. The implementation solution is acquiring a map to be detected; acquiring traffic indication information marked on the map; acquiring actual road test data of each detection area on the map; acquiring a vehicle driving behavior of each detection area according to the actual road test data of each detection area; and generating a detection result of the map according to the traffic indication information and the vehicle driving behavior corresponding to each detection area. Thus, automatic detection of the map can be realized by the traffic indication information marked on the map and the vehicle driving behavior corresponding to each detection area, and the detection efficiency and the detection accuracy of high definition map can be improved.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N<M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

Abstract: A method and an apparatus for processing information are provided. A method may include: sending a preset data acquisition request to a target data transmission end included in at least two preset data transmission ends; receiving feedback information from the target data transmission end, and determining whether the feedback information includes the acquired data; determining, in response to determining that the feedback information does not include the acquired data, whether the feedback information includes first fault information for indicating that the target data transmission end malfunctions; and selecting, in response to determining that the feedback information includes the first fault information, a data transmission end other than the target data transmission end from the at least two data transmission ends as a new target data transmission end.

Abstract: The present disclosure provides an integrated positioning method and system. The method comprises: receiving IMU data, and solving for the IMU data in a first Kalman filter to obtain a system state variable of an autonomous vehicle; receiving and buffering measurement data, and regarding the measurement data as current measurement data; performing measurement updating: obtaining the system state variable at a measurement time of current measurement data, and solving in the second Kalman filter to obtain an updated system state variable; if measurement data after the measurement time of the measurement data is already buffered, regarding the already-buffered measurement data after the measurement time as current measurement data, and performing the measurement update. The present disclosure can solve the problem about measurement information disorder caused by inconsistency of time delay in solving between different sensors, and improve real time and accuracy of the positioning.

Abstract: The present disclosure provides a method and a device for positioning, an apparatus and a computer readable storage medium. The method includes acquiring a visual feature map of a geographic area located by a movable object. The visual feature map includes a first set of visual features. The method further includes acquiring at least one image captured by a camera coupled to the movable object during a motion of the movable object within the geographic area. The method further includes extracting a second set of visual features corresponding to the first set of visual features from the at least one image. In addition, the method further includes determining at least one pose of the movable object during the motion at least based on the visual feature map and the second set of visual features.

Abstract: In a specific implementation of the method a reflectance map is constructed based on a position and an Euler angle, obtained through a global optimization and used for constructing a reflectance map, of a center of a laser radar corresponding to each frame laser point cloud used for constructing the reflectance map collected in each collection region. This implementation implements the level-by-level pose optimization of laser point clouds used for constructing a reflectance map that are collected in each collection region in an excessively large region, to obtain an accurate position and Euler angle, used for constructing the reflectance map, of a laser radar center corresponding to each frame laser point cloud used for constructing the reflectance map.

Abstract: The present disclosure provides a laser point cloud positioning method and system. The method comprises: converting laser point cloud reflection value data and height value data matched with a current location of an autonomous vehicle into laser point cloud projection data in a ground plane; assigning a weight for a reflection value matching probability and a height value matching probability of the laser point cloud projection data and a laser point cloud two-dimensional grid map, and determining a matching probability of the laser point cloud projection data and the laser point cloud two-dimensional grid map; determining a location of the autonomous vehicle in the laser point cloud two-dimensional grid map based on a matching probability of the laser point cloud projection data and the laser point cloud two-dimensional grid map.