Methods and systems are provided for mobile platforms. A mobile platform comprises a body and a radar system. The body includes a wheel assembly, and the radar system is installed on the wheel assembly.

A data processing system including a data processing apparatus that can generate an accurate road surface displacement correlating value that is used for a preview damping control is provided. The system includes a cloud having a data processing section and a processing-data-base-section for temporally storing data. The data processing apparatus stores sensing data in the processing-data-base-section, wherein the sensing data is a chunk of sensor values from which a road surface displacement correlating value correlating with a vertical displacement of a road surface on which said vehicle is traveling can be calculated. The sensor values are sequentially and successively detected/obtained by various sensors of the vehicle. The data processing section performs specific offline data processing for a chunk of the sensing data stored in the processing-data-base-section so as to generate data of the road surface displacement correlating value from the sensing data.

Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.

Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.

Methods and systems are described that enable radar reference map generation. A radar occupancy grid is received, and radar attributes are determined from occupancy probabilities within the radar occupancy grid. Radar reference map cells are formed, and the radar attributes are used to determine Gaussians for the radar reference map cells that contain a plurality of the radar attributes. A radar reference map is then generated that includes the Gaussians determined for the radar referenced map cells that contain the plurality of radar attributes. By doing so, the generated radar reference map is accurate while being spatially efficient.

Methods and systems are described that enable radar reference map generation. A radar occupancy grid is received, and radar attributes are determined from occupancy probabilities within the radar occupancy grid. Radar reference map cells are formed, and the radar attributes are used to determine Gaussians for the radar reference map cells that contain a plurality of the radar attributes. A radar reference map is then generated that includes the Gaussians determined for the radar referenced map cells that contain the plurality of radar attributes. By doing so, the generated radar reference map is accurate while being spatially efficient.

Various implementations described herein generate training instances that each include corresponding training instance input that is based on corresponding sensor data of a corresponding autonomous vehicle, and that include corresponding training instance output that is based on corresponding sensor data of a corresponding additional vehicle, where the corresponding additional vehicle is captured at least in part by the corresponding sensor data of the corresponding autonomous vehicle. Various implementations train a machine learning model based on such training instances. Once trained, the machine learning model can enable processing, using the machine learning model, of sensor data from a given autonomous vehicle to predict one or more properties of a given additional vehicle that is captured at least in part by the sensor data.

The present invention relates to a dual-speed final drive control method and terminal device, and a storage medium. The method includes: S1: acquiring, according to electronic horizon data ahead, a gradient value of a road ahead, and when an absolute value of the gradient value is greater than a gradient threshold, proceeding to S2; and S2: determining, according to the gradient value, whether the road ahead is an uphill section or a downhill section, and if the road ahead is the uphill section, controlling all gears of a transmission to correspond to a higher final drive ratio in the dual-speed final drive when a vehicle travels into the uphill section; and if the road ahead is the downhill section, controlling all the gears of the transmission to correspond to a lower final drive ratio in the dual-speed final drive when the vehicle travels into the uphill section. According to the present invention, information of a road gradient predicted by electronic horizon is used in control of dynamic matching between a speed ratio of the dual-speed final drive and a speed ratio of the transmission, thereby making use of the dual-speed-ratio final drive to the greatest extent according to the terrain to improve the economy in energy consumption of the entire vehicle.

A method of and system for processing Light Detection and Ranging (LIDAR) point cloud data. The method is executable by an electronic device, communicatively coupled to a LIDAR installed on a vehicle, the LIDAR having a plurality of lasers for capturing LIDAR point cloud data. The method includes receiving a first LIDAR point cloud data captured by the LIDAR; executing a Machine Learning Algorithm (MLA) for: analyzing a first plurality of LIDAR points of the first point cloud data in relation to a response pattern of the plurality of lasers; retrieving a grid representation data of a surrounding area of the vehicle; determining if the first plurality of LIDAR points is associated with a blind spot, the blind spot preventing a detection algorithm of the electronic device to detect presence of at least one object surrounding the vehicle conditioned on the at least one object is present.

A method of and system for processing Light Detection and Ranging (LIDAR) point cloud data. The method is executable by an electronic device, communicatively coupled to a LIDAR installed on a vehicle, the LIDAR having a plurality of lasers for capturing LIDAR point cloud data. The method includes receiving a first LIDAR point cloud data captured by the LIDAR; executing a Machine Learning Algorithm (MLA) for: analyzing a first plurality of LIDAR points of the first point cloud data in relation to a response pattern of the plurality of lasers; retrieving a grid representation data of a surrounding area of the vehicle; determining if the first plurality of LIDAR points is associated with a blind spot, the blind spot preventing a detection algorithm of the electronic device to detect presence of at least one object surrounding the vehicle conditioned on the at least one object is present.