Training a machine learning based model of a vehicle perception component based on sensor settings

A non-transitory computer-readable medium storing thereon instructions executable by one or more processors to implement a training procedure for training a perception component, the training procedure comprising:

• generating a first set of training data that includes (i) first sensor data indicative of real or simulated vehicle environments, the first sensor data corresponding to a first setting of one or more sensor parameters, the first setting defining a first spatial distribution of scan lines within a point cloud, the first spatial distribution including:
• a uniform distribution, a sampling of a continuous mathematical distribution, or a plurality of regions each having a different uniform spatial distribution, and (ii) an indicator of the first setting;
• generating a second set of training data that includes (i) second sensor data indicative of real or simulated vehicle environments, the second sensor data corresponding to a second setting of the one or more sensor parameters, the second setting defining a second spatial distribution of scan lines within a point cloud, the second spatial distribution of scan lines being different than the first spatial distribution of scan lines, and (ii) an indicator of the second setting;
• and
• training the perception component, at least in part by training a machine learning based model of the perception component using the first and second sets of training data,
• wherein the trained perception component is configured to generate signals descriptive of a current state of an environment, as a vehicle moves through the environment, by processing (i) sensor data generated by one or more sensors of the vehicle, the sensor data including point clouds generated by the one or more sensors and (ii) one or more indicators indicating which setting of the one or more sensor parameters corresponds to which portions of the generated sensor data including indicating which spatial distributions correspond to which of the point clouds generated by the one or more sensors.

A vehicle comprising:

• one or more sensors configured to generate sensor data by sensing an environment through which the vehicle is moving, including at least a first sensor;
• one or more operational subsystems;
• and
• a computing system configured to
• receive the sensor data,
• generate, using a trained perception component and based on the received sensor data, signals descriptive of a current state of an environment, the perception component being trained using
• a first set of training data that includes (i) first sensor data indicative of real or simulated vehicle environments, the first sensor data corresponding to a first setting of one or more sensor parameters, the first setting defining a first spatial distribution of scan lines within a point cloud, the first spatial distribution including:
• a uniform distribution, a sampling of a continuous mathematical distribution, or a plurality of regions each having a different uniform spatial distribution, and (ii) an indicator of the first setting, and
• a second set of training data that includes (i) second sensor data indicative of real or simulated vehicle environments, the second sensor data corresponding to a second setting of the one or more sensor parameters, the second setting defining a second spatial distribution of scan lines within a point cloud, the second spatial distribution of scan lines being different than the first spatial distribution of scan lines, and (ii) an indicator of the second setting,
• wherein the trained perception component is configured to generate the signals descriptive of the current state of the environment by processing (i) point clouds generated by the one or more sensors and (ii) one or more indicators indicating which spatial distributions correspond to which of the point clouds generated by the one or more sensors,
• generate driving decisions based on the signals descriptive of the current state of the environment, and
• cause the one or more operational subsystems to maneuver the vehicle in accordance with the generated driving decisions.