Machine learning based occupancy grid generation

A device, comprising:

• one or more memories;
• and
• one or more processors, coupled to the one or more memories, configured to:
• receive sensor data associated with a vehicle and a set of frames,
• wherein the sensor data indicates one or more sensor detections;
• aggregate, using a first pose, the sensor data associated with the set of frames to generate an aggregated frame,
• wherein the aggregated frame includes a set of cells, and
• wherein the aggregated frame includes data for a subset of cells associated with the one or more sensor detections;
• generate, using a machine learning model, an occupancy grid,
• wherein the machine learning model is trained using a loss function that calculates a loss using only cells associated with a known occupancy status, and
• wherein an output of the machine learning model includes a probability of one or more occupancy statuses for respective cells from the set of cells;
• and
• perform an action based on the occupancy grid.

A method, comprising:

• receiving, by a device, sensor data associated with a vehicle and a set of frames,
• wherein the sensor data indicates one or more sensor detections;
• aggregating, using a first pose, the sensor data associated with the set of frames to generate an aggregated frame,
• wherein the aggregated frame is associated with a set of cells;
• obtaining, by the device, an indication of a respective occupancy label for each cell from the set of cells,
• wherein the respective occupancy label includes a first occupancy label indicating a known occupancy status or a second occupancy label indicating an unknown occupancy status, and
• wherein a subset of cells from the set of cells are associated with the first occupancy label;
• training, using data associated with the aggregated frame, a machine learning model to generate an occupancy grid,
• wherein training the machine learning model is associated with a loss function that calculates a loss for respective cells from the subset of cells, and
• wherein the machine learning model is trained to predict a probability of an occupancy status for respective cells from the set of cells;
• and
• providing, by the device and to another device, the machine learning model.

A method, comprising:

• receiving, by a device, sensor data associated with a vehicle and a set of frames,
• wherein the sensor data indicates one or more sensor detections;
• aggregating, using a first pose, the sensor data associated with the set of frames to generate an aggregated frame,
• wherein the aggregated frame includes a set of cells, and
• wherein the aggregated frame includes data for a subset of cells associated with the one or more sensor detections;
• generating, using a machine learning model, an occupancy grid,
• wherein the machine learning model is trained using a loss function that calculates a loss using only cells associated with a known occupancy status, and
• wherein an output of the machine learning model includes a probability of one or more occupancy statuses for respective cells from the set of cells;
• and
• performing, by the device, an action based on the occupancy grid.