Abstract: A system for detecting occupancy of a vehicle travelling in a direction of travel along a road. The system includes a roadside imaging device positioned on a roadside, and a first roadside light emitter, and a roadside vehicle detector. A processor is configured to receive a signal from the roadside vehicle detector, command the first roadside light emitter to emit light according to a first pattern for a first duration, command the roadside imaging device to capture images of the side of the vehicle, and compute a vehicle occupancy, in each of the captured images by determining one or more regions of interest in each of the captured images, and determining a number of visible occupants in the one or more regions of interest. The processor determines a most likely number of occupants based on each determined vehicle occupancy.

Abstract: A system for detecting occupancy of a vehicle travelling in a direction of travel along a road. The system includes a roadside imaging device positioned on a roadside, and a first roadside light emitter, and a roadside vehicle detector. A processor is configured to receive a signal from the roadside vehicle detector, command the first roadside light emitter to emit light according to a first pattern for a first duration, command the roadside imaging device to capture images of the side of the vehicle, and compute a vehicle occupancy, in each of the captured images by determining one or more regions of interest in each of the captured images, and determining a number of visible occupants in the one or more regions of interest. The processor determines a most likely number of occupants based on each determined vehicle occupancy.

Abstract: A system for detecting occupancy of a vehicle travelling in a direction of travel along a road. The system includes a roadside imaging device positioned on a roadside, and a first roadside light emitter, and a roadside vehicle detector. A processor is configured to receive a signal from the roadside vehicle detector, command the first roadside light emitter to emit light according to a first pattern for a first duration, command the roadside imaging device to capture images of the side of the vehicle, and compute a vehicle occupancy, in each of the captured images by determining one or more regions of interest in each of the captured images, and determining a number of visible occupants in the one or more regions of interest. The processor determines a most likely number of occupants based on each determined vehicle occupancy.

Abstract: A spatial monitoring system for a vehicle includes a LiDAR sensor, sensors arranged to monitor ambient environmental states, sensors arranged to monitor vehicle operating conditions, and a controller. The controller includes an instruction set that is executable to monitor the ambient environmental states and the vehicle operating conditions. The LiDAR sensor captures a point cloud, and the instruction set is executable to determine point cloud metrics. Desired control parameters for the LiDAR sensor are determined based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics, and the LiDAR sensor is controlled based upon the desired control parameters. The LiDAR sensor is controlled to capture an image of a field-of-view proximal to the vehicle based upon the desired control parameters.

Abstract: A system for detecting occupancy of a vehicle travelling in a direction of travel along a road. The system includes a roadside imaging device positioned on a roadside, and a first roadside light emitter, and a roadside vehicle detector. A processor is configured to receive a signal from the roadside vehicle detector, command the first roadside light emitter to emit light according to a first pattern for a first duration, command the roadside imaging device to capture images of the side of the vehicle, and compute a vehicle occupancy, in each of the captured images by determining one or more regions of interest in each of the captured images, and determining a number of visible occupants in the one or more regions of interest. The processor determines a most likely number of occupants based on each determined vehicle occupancy.

Abstract: A spatial monitoring system for a vehicle includes a LiDAR sensor, sensors arranged to monitor ambient environmental states, sensors arranged to monitor vehicle operating conditions, and a controller. The controller includes an instruction set that is executable to monitor the ambient environmental states and the vehicle operating conditions. The LiDAR sensor captures a point cloud, and the instruction set is executable to determine point cloud metrics. Desired control parameters for the LiDAR sensor are determined based upon the ambient environmental conditions, the vehicle operating conditions, and the point cloud metrics, and the LiDAR sensor is controlled based upon the desired control parameters. The LiDAR sensor is controlled to capture an image of a field-of-view proximal to the vehicle based upon the desired control parameters.

Abstract: A method to automate detection of a vehicle light degradation or failure includes generating a distinct pattern to be executed by at least one vehicle light, and sending a command to a light control module operative to cause the at least one vehicle light to emit the distinct pattern. The method continues with extracting features from camera images of the distinct pattern emitted by the at least one vehicle light, and then comparing the features extracted from the camera images with features from the commanded distinct pattern to determine degradation or failure of the at least one vehicle light's state of health.

Abstract: A method to automate detection of a vehicle light degradation or failure includes generating a distinct pattern to be executed by at least one vehicle light, and sending a command to a light control module operative to cause the at least one vehicle light to emit the distinct pattern. The method continues with extracting features from camera images of the distinct pattern emitted by the at least one vehicle light, and then comparing the features extracted from the camera images with features from the commanded distinct pattern to determine degradation or failure of the at least one vehicle light's state of health.