Abstract: A method for displaying lane information for a vehicle, includes obtaining a digital map; defining localization information of the vehicle; obtaining a route plan of the vehicle; determining a driving lane according to the localization information of the vehicle and the route plan of the vehicle; obtaining coordinates information of lane markings of the driving lane from the digital map; receiving image data from a camera mounted on the vehicle; transforming the lane markings of the driving lane from a coordinate system of the digital map to a coordinate system of the camera; generating a lane guide sign indicating the lane markings of the driving lane in the coordinate system of the camera based on the lane markings of the driving lane transformed to the coordinate system of the camera; and superimposing the lane guide sign of the lane markings of the driving lane on the image data received from the camera.

Abstract: A method and a system for lane change prediction. The method includes collecting raw driving data, extracting a plurality of feature sets from the collected raw driving data, and obtaining corresponding lane change information. The lane change information indicates a status of lane change of a vehicle under each of the extracted feature sets. The method further includes automatically labeling each of the extracted plurality sets of features with the obtained corresponding lane change information. The method further includes training a lane change prediction model with the labeled plurality sets of features. Examples of the present disclosure further describe methods, systems, and vehicles for applying the lane change prediction model.

Abstract: A method and an apparatus optimizes scan data obtained by sensors on vehicle, and corrects trajectory for a vehicle/robot based on the optimized scan data.

Abstract: A method for segmentation of a point cloud includes receiving a first frame of point cloud from a sensor; segmenting the first frame of point cloud to obtain a first set of point clusters representing a segmentation result for the first frame of point cloud; receiving a second frame of point cloud from the sensor; mapping the first set of point clusters to the second frame of point cloud; determining points within the second frame of point cloud which do not belong to the mapped first set of point clusters; segmenting the points within the second frame of point cloud which do not belong to the mapped first set of point clusters to obtain a second set of point clusters; and generating a segmentation result for the second frame of point cloud by combining the first set of point clusters and the second set of point clusters.

Abstract: A method for determining driving strategy of a vehicle includes obtaining localization information of the vehicle, a driving route of the vehicle, and a current driving velocity of the vehicle. The method further includes detecting and interpreting a current status of a traffic light. The method further includes recognizing a duration of the current status of the traffic light. The method further includes detecting a relevant stop line. The method further includes obtaining a distance between the stop line and the vehicle. The method further includes determining whether to change the current driving velocity according to the current status of the traffic light, the duration of the current status of the traffic light, the distance between the traffic stop line and the vehicle, and the current driving velocity. A data processing device for determining driving strategy of a vehicle is also provided.

Abstract: A method and an apparatus identifies an object. The method includes at a first time instant, acquiring raw data output by a camera installed on a vehicle, the output raw data comprises one of a continuous data output format for a continuous model or a discrete data output format for a discrete model; feeding the raw data to a discrete statistic model to form discrete statistic model format data for the first time instant, the discrete statistic model format data includes a continuous part having the continuous data output format and a discrete part having the discrete data output format; fusing the discrete statistic model format data for the first time instant with historical discrete statistic model format data to update the discrete statistic model format data for the first time instant; and identifying the object from the updated discrete statistic model format data for the first time instant, wherein the object includes a lanemarking characterized by a curve.

Abstract: A method for determining a 6-dimensional (6D) delta pose of a vehicle includes obtaining a pre-defined roadmodel 2-dimensional (2D) manifold in 6D space, wherein the roadmodel 2D manifold in 6D space is pre-defined by transforming objects in a roadmodel from 3D (x, y, z) space into 6D (x, y, z, r, p, y) space, wherein x and y represent positions of objects on a horizontal plane, and z, r, p and y represent the height, roll angle, pitch angle and yaw angle of the objects in real world; obtaining a delta 3D odometry (?x, ?y, ?) from an odometer of the vehicle, wherein ?x and ?y represent the movements in the lateral and forward-reverse directions, and ? represents a current heading of the vehicle; projecting the obtained delta 3D odometry onto the roadmodel 2D manifold; and determining the 6D delta pose of the vehicle corresponding to the delta 3D odometry.

Abstract: A method includes obtaining a pre-defined roadmodel 2-dimensional (2D) manifold in 6D space, wherein the roadmodel 2D manifold in 6D space is pre-defined by transforming objects in a roadmodel from 3D space into 6D space, obtaining a current position of the vehicle and a target position of the vehicle on the horizontal plane; obtaining a piece of the roadmodel 2D manifold covering the current position and the target position; flattening the piece of the roadmodel represented by the piece of the roadmodel 2D manifold onto the horizontal plane while maintaining a distance between any two points from 3D space to the horizontal plane; and planning a trajectory for the vehicle from the current position to the target position in the flattened roadmodel on the horizontal plane.

Abstract: A method and apparatus for selecting a map from a plurality of maps having different resolutions for a moving object includes dynamically selecting an appropriate map from maps having different levels of details according to environment information, such that a minimum amount of required map is loaded, thereby effectively improving the data processing efficiency of vehicles.

Abstract: A computer-implemented method for representing environmental elements includes receiving scan data comprising at least a point cloud representing at least an environmental element from a sensor, segmenting the point cloud into point clusters, and partitioning the point clusters into hierarchical grids. The method also includes establishing a Gaussian distribution for points in each cell of each of the hierarchical grids, and constructing a Gaussian Mixture Model based on the Gaussian distribution for representing the environmental element.

Abstract: A method for segmentation of a point cloud, includes: receiving a first frame of point cloud from a sensor; segmenting the first frame of point cloud using a segmentation algorithm to obtain a first set of point clusters representing a segmentation result for the first frame of point cloud; receiving a second frame of point cloud from the sensor; mapping the first set of point clusters to the second frame of point cloud; determining points within the second frame of point cloud which do not belong to the mapped first set of point clusters; segmenting the points within the second frame of point cloud which do not belong to the mapped first set of point clusters using the segmentation algorithm to obtain a second set of point clusters; and generating a segmentation result for the second frame of point cloud by combining the first set of point clusters and the second set of point clusters.

Abstract: A method for configuring a sensor, which is adapted to be installed on a moving object, includes obtaining localization information of the sensor from a localization device, obtaining characteristic information and localization information of at least one element from a digital map, and choosing one or more elements of the at least one element in the digital map. The method also includes configuring the sensor according to the characteristic information and/or the localization information of the one or more chosen elements.

Abstract: A method and a system for lane change prediction. The method includes collecting raw driving data, extracting a plurality of feature sets from the collected raw driving data, and obtaining corresponding lane change information. The lane change information indicates a status of lane change of a vehicle under each of the extracted feature sets. The method further includes automatically labeling each of the extracted plurality sets of features with the obtained corresponding lane change information. The method further includes training a lane change prediction model with the labeled plurality sets of features. Examples of the present disclosure further describe methods, systems, and vehicles for applying the lane change prediction model.

Abstract: A method for displaying lane information for a vehicle, includes obtaining a digital map; defining localization information of the vehicle; obtaining a route plan of the vehicle; determining a driving lane according to the localization information of the vehicle and the route plan of the vehicle; obtaining coordinates information of lane markings of the driving lane from the digital map; receiving image data from a camera mounted on the vehicle; transforming the lane markings of the driving lane from a coordinate system of the digital map to a coordinate system of the camera; generating a lane guide sign indicating the lane markings of the driving lane in the coordinate system of the camera based on the lane markings of the driving lane transformed to the coordinate system of the camera; and superimposing the lane guide sign of the lane markings of the driving lane on the image data received from the camera.

Abstract: A method and an apparatus for correcting deformation of a digital map for a vehicle, includes loading a related part from the digital map; determining whether the related part includes road coordinate information in a first zone and road coordinate information in a second zone, wherein the road coordinate information in the first zone is associated with a first Cartesian coordinate system and the road coordinate information in the second zone is associated with a second Cartesian coordinate system; transforming the road coordinate5 information in the second zone to the first Cartesian coordinate system, if the related part includes the road coordinate information in the first zone and the road coordinate information in the second zone; and generating a corrected map by combining the road coordinate information in the first zone and the transformed road coordinate information in the second zone; wherein the first Cartesian coordinate system is the coordinate system of the vehicle for processing the road coordi

Abstract: A method for displaying Point of Interest (POI) information for a vehicle, includes: obtaining a digital map; obtaining localization information of the vehicle; obtaining coordinate information of the POI and type information of the POI from the digital map; receiving reality image data from a camera mounted on the vehicle; transforming the coordinate information of the POI from a coordinate system of the digital map to a coordinate system of the camera; generating a guide sign indicating the POI in the coordinate system of the camera based on the coordinates information of the POI transformed to the coordinate system of the camera and the type information of the POI; and superimposing the guide sign indicating the POI on the image data received from the camera.

Abstract: A method for determining driving strategy of a vehicle includes obtaining localization information of the vehicle, a driving route of the vehicle, and a current driving velocity of the vehicle. The method further includes detecting and interpreting a current status of a traffic light. The method further includes recognizing a duration of the current status of the traffic light. The method further includes detecting a relevant stop line. The method further includes obtaining a distance between the stop line and the vehicle. The method further includes determining whether to change the current driving velocity according to the current status of the traffic light, the duration of the current status of the traffic light, the distance between the traffic stop line and the vehicle, and the current driving velocity. A data processing device for determining driving strategy of a vehicle is also provided.

Abstract: A method and an apparatus for uncertainty modeling of a point cloud and a non-transient storage medium are provided. The method finds the neighboring points for each point; fits a local quadratic surface for each point; and generates a Gaussian Model (GM) for representing the uncertainty modeling of the local surface at each point. The GM is constructed in a piece-wise manner on the underlying surface of the point cloud, which will accurately model the surface uncertainty.

Abstract: A computer-implemented method for constructing an environment model includes generating a first Signatured Gaussian Mixture (SGM) model corresponding to a first part of the environment based on a first sensor data, receiving a second SGM model corresponding to a second part of the environment, and constructing a third SGM model comprising the first SGM model and the second SGM model.

Abstract: A method and apparatus for selecting a map from a plurality of maps having different resolutions for a moving object includes dynamically selecting an appropriate map from maps having different levels of details according to environment information, such that a minimum amount of required map is loaded, thereby effectively improving the data processing efficiency of vehicles.