Abstract: A current set of radar data may be combined with previous sets of radar data to create a combined set of radar data. Each of these sets of radar data and the combined set of radar data may include various data points and each of these data points may be associated with certain specific features or classifications. The set of combined data may then be pre-processed to identify distances to associate with certain data points, locations to associate with those data points, and velocities associated with the data points such that each of the respective data points may be mapped and tagged with data that identifies identify positions, velocities, and other physical details of the respective data points. This pre-processed data may then be processed by a machine learning process to identify kinematic information that may then be provided to a tracking system of an AV.

Abstract: The present disclosure is directed to processing data associated with a non-radar type sensing device to identify data points associated with a radar type sensing device that are likely secondary radar reflections such that a processor of a sensing apparatus can direct processing resources to processing radar data that are associated with primary radar reflections. The receipt of secondary radar reflections may cause a processor of a sensing apparatus to identify that an object is located at a location when there is no object in that location. Because of this, methods and apparatus of the present disclosure identify and avoid processing radar data that are likely to be associated with a object that does not exist. Eliminating false radar data, therefore, can prevent a processor of a sensing apparats from performing unnecessary processing tasks and can help prevent that processor from making false determinations.

Abstract: Depth data processing systems and methods are disclosed. A mapping system receives, from one or more depth sensors, depth sensor data that includes a plurality of points corresponding to an environment. The mapping system uses one or more trained machine learning models to perform semantic segmentation of the plurality of points, to classify a first subset of the points into a first category and to classify a second subset of the points into a second category. The mapping system clusters the plurality of points into a plurality of clusters based on the semantic segmentation. At least some of the first subset of the points are clustered into a first cluster and at least some of the second subset of the points are clustered into a second cluster. The mapping system generates a map of at least a portion of the environment based on the plurality of clusters.

Abstract: A method and device for controlling a vehicle, and an autonomous driving vehicle are provided. The method includes that: historical weather information of a present driving road planned for a vehicle is acquired; a slippery parameter of the present driving road is determined according to the historical weather information; and an autonomous driving mode of the vehicle is controlled according to the slippery parameter.

Abstract: A method and device for controlling a vehicle, and an autonomous driving vehicle are provided. The method includes that: historical weather information of a present driving road planned for a vehicle is acquired; a slippery parameter of the present driving road is determined according to the historical weather information; and an autonomous driving mode of the vehicle is controlled according to the slippery parameter.