Method and system for operating an on-vehicle LiDAR sensor

1. A spatial monitoring system for a vehicle, the spatial monitoring system comprising:

a LiDAR sensor;

a first plurality of sensors arranged to monitor ambient environmental states, including one or more of temperature, pressure, humidity, and precipitation;

a second plurality of sensors arranged to monitor vehicle operating conditions;

a controller, in communication with the first plurality of sensors and the second plurality of sensors, and operatively connected to the LiDAR sensor, the controller including an instruction set, the instruction set being executable to:

monitor, via the first plurality of sensors, the ambient environmental states,

monitor, via the second plurality of sensors, the vehicle operating conditions,

capture, via the LiDAR sensor, a point cloud,

determine point cloud metrics for the point cloud, and determine desired control parameters for the LiDAR sensor based upon the ambient

environmental conditions, the vehicle operating conditions, and the point cloud metrics, and control the LiDAR sensor to capture an image of a field-of-view proximal to the vehicle based upon the desired control parameters.

2. The spatial monitoring system of claim 1 , wherein the LiDAR sensor comprises a mechanical spinning LiDAR sensor, wherein the desired control parameters comprise a spinning rate and a laser power level; and

wherein the instruction set being executable to determine the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions and the point cloud metrics comprises the instruction set being executable to determine the desired control parameters for one of the spinning rate and the laser power level of the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions and the point cloud metrics.

3. The spatial monitoring system of claim 1 , wherein the LiDAR sensor comprises a solid state LiDAR sensor, wherein the desired control parameters comprise a scanning pattern, a scan density across the field-of-view, a frame rate, and a laser power for the LiDAR sensor, and

wherein the instruction set being executable to determine the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions and the point cloud metrics comprises the instruction set being executable to determine the desired control parameters for one of the scanning pattern, the scan density across the field-of-view, the frame rate, and the laser power for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions and the point cloud metrics.

4. The spatial monitoring system of claim 1 , wherein the first plurality of sensors arranged to monitor ambient environmental states comprises a plurality of sensors configured to monitor ambient pressure and temperature.

5. The spatial monitoring system of claim 1 , wherein the second plurality of sensors arranged to monitor vehicle operating conditions comprises a plurality of sensors configured to monitor vehicle wheel speed and steering angle.

6. The spatial monitoring system of claim 1 , wherein the instruction set being executable to determine point cloud metrics of the LiDAR sensor comprises the instruction set being executable to determine a quantity of points with zero range or reflectivity values, mean, median, standard deviation and a minimum/maximum interval of range and reflectivity for nonzero measurements for the point cloud of the LiDAR sensor.

7. The spatial monitoring system of claim 1 , wherein the instruction set being executable to determine the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics of the LiDAR sensor comprises the instruction set being executable to employ a reinforcement learning routine in a simulation pre-training mode to determine the desired control parameters for the LiDAR sensor that maximize the point cloud metrics of the LiDAR sensor based upon the ambient environmental conditions and the vehicle operating conditions.

8. The spatial monitoring system of claim 1 , wherein the instruction set being executable to determine the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics of the LiDAR sensor comprises the instruction set being executable to employ a reinforcement learning routine in-use to determine the desired control parameters for the LiDAR sensor that maximize the point cloud metrics of the LiDAR sensor based upon the ambient environmental conditions and the vehicle operating conditions.

9. A vehicle, comprising:

an advanced driver assistance system (ADAS) and a spatial monitoring system;

wherein the spatial monitoring system includes:

a LiDAR sensor;

a first plurality of sensors arranged to monitor ambient environmental states, including one or more of temperature, pressure, humidity, and precipitation;

a second plurality of sensors arranged to monitor vehicle operating conditions; and

a controller, in communication with the first plurality of sensors and the second plurality of sensors, and operatively connected to the LiDAR sensor, the controller including an instruction set, the instruction set being executable to:

monitor, via the first plurality of sensors, the ambient environmental states, monitor, via the second plurality of sensors, the vehicle operating conditions, capture, via the LiDAR sensor, a point cloud,

determine metrics for the point cloud, and

determine desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics, and

control the LiDAR sensor to capture an image of a field-of-view proximal to the vehicle based upon the desired control parameters;

wherein the ADAS controls operation of the vehicle based upon the image of a field-of- view proximal to the vehicle.

10. The vehicle of claim 9 , wherein the LiDAR sensor comprises a mechanical spinning LiDAR sensor, wherein the desired control parameters comprise a spinning rate and a laser power level; and

wherein the instruction set being executable to determine the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions and the point cloud metrics comprises the instruction set being executable to determine the desired control parameters for one of the spinning rate and the laser power level of the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions and the point cloud metrics.

11. The vehicle of claim 9 , wherein the LiDAR sensor comprises a solid state LiDAR sensor, wherein the desired control parameters comprise a scanning pattern, a scan density across the field-of-view, a frame rate, and a laser power for the LiDAR sensor, and

wherein the instruction set being executable to determine the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions and the point cloud metrics comprises the instruction set being executable to determine the desired control parameters for one of the scanning pattern, the scan density across the field-of-view, the frame rate, and the laser power for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions and the point cloud metrics.

12. The vehicle of claim 9 , wherein the first plurality of sensors arranged to monitor ambient environmental states comprises a plurality of sensors configured to monitor ambient pressure and temperature.

13. The vehicle of claim 9 , wherein the second plurality of sensors arranged to monitor vehicle operating conditions comprises a plurality of sensors configured to monitor vehicle wheel speed and steering angle.

14. The vehicle of claim 9 , wherein the instruction set being executable to determine point cloud metrics of the LiDAR sensor comprises the instruction set being executable to determine a quantity of points with zero range or reflectivity values, mean, median, standard deviation and a minimum/maximum interval of range and reflectivity for nonzero measurements for the point cloud of the LiDAR sensor.

15. The vehicle of claim 9 , wherein the instruction set being executable to determine the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics of the LiDAR sensor comprises the instruction set being executable to employ a reinforcement learning routine in a simulation pre-training mode to determine the desired control parameters for the LiDAR sensor that maximize the point cloud metrics of the LiDAR sensor based upon the ambient environmental conditions and the vehicle operating conditions.

16. The vehicle of claim 9 , wherein the instruction set being executable to determine the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics of the LiDAR sensor comprises the instruction set being executable to employ a reinforcement learning routine in-use to determine the desired control parameters for the LiDAR sensor that maximize the point cloud metrics of the LiDAR sensor based upon the ambient environmental conditions and the vehicle operating conditions.

17. A method for monitoring a spatial environment for a vehicle, method comprising:

monitoring a plurality of ambient environmental states, including one or more of temperature, pressure, humidity, and precipitation;

monitoring vehicle operating conditions;

capturing, via a LiDAR sensor, a point cloud,

determining metrics for the point cloud, and

determining desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics, and controlling the LiDAR sensor to capture an image of a field-of-view proximal to the vehicle based upon the desired control parameters.

18. The method of claim 17 ,

wherein monitoring vehicle operating conditions includes monitoring an ambient pressure and ambient temperature;

wherein monitoring vehicle operating conditions comprises monitoring vehicle wheel speed and steering angle; and

wherein determining metrics for the point cloud comprises determining a quantity of points with zero range or reflectivity values, and a mean, a median, a standard deviation, and a minimum/maximum interval of range and reflectivity for nonzero measurements for the point cloud of the LiDAR sensor.

19. The method of claim 17 , wherein determining the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics of the LiDAR sensor comprises executing a reinforcement learning routine in a simulation pre-training mode to determine the desired control parameters for the LiDAR sensor that maximize the point cloud metrics of the LiDAR sensor based upon the ambient environmental conditions and the vehicle operating conditions.

20. The method of claim 17 , determining the desired control parameters for the LiDAR sensor based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics of the LiDAR sensor comprises executing a reinforcement learning routine in-use to determine the desired control parameters for the LiDAR sensor that maximize the point cloud metrics of the LiDAR sensor based upon the ambient environmental conditions and the vehicle operating conditions.