Range-view LIDAR-based object detection

An object detection system comprising:

• one or more processors;
• a machine-learned prediction model, wherein the prediction model has been trained to receive a data matrix comprising multiple channels of LIDAR-associated data and, in response to receipt of the data matrix comprising multiple channels of LIDAR-associated data, output one or more class predictions for different portions of the data matrix;
• and
• at least one tangible, non-transitory computer readable medium that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
• obtaining a data matrix comprising multiple channels of LIDAR-associated data, wherein at least one of the multiple channels of LIDAR-associated data comprises LIDAR Background Subtraction foreground data indicative of whether a LIDAR data point is a foreground LIDAR data point remaining after LIDAR Background Subtraction is applied to the LIDAR data received from the one or more LIDAR sensors;
• inputting the data matrix comprising multiple channels of LIDAR-associated data into the machine-learned prediction model;
• and
• receiving, as output of the machine-learned prediction model, one or more class predictions for one or more different portions of the data matrix.

An autonomous vehicle comprising:

• a sensor system comprising at least one LIDAR sensor configured to transmit ranging signals relative to the autonomous vehicle and to generate LIDAR data comprising a plurality of LIDAR data points;
• and
• a vehicle computing system comprising:
• one or more processors;
• and
• at least one tangible, non-transitory computer readable medium that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
• receiving LIDAR data from the sensor system;
• generating a data matrix comprising a plurality of data channels based at least in part on the LIDAR data, wherein at least one of the plurality of data channels within the data matrix comprises LIDAR Background Subtraction foreground data indicative of whether each of the plurality of LIDAR data points is a foreground LIDAR data point remaining after LIDAR Background Subtraction is applied to LIDAR data from the sensor system;
• providing the data matrix comprising the plurality of data channels as input to a machine-learned model;
• receiving, as a first output of the machine-learned model, a class prediction for each cell of the data matrix;
• and
• receiving, as a second output of the machine-learned model, a properties estimation for each cell of the data matrix.