A system for controlling a pixelated lighting device (1) is configured to determine a location of a further light source (29) relative to the pixelated lighting device and obtain original light settings (91-99) for the pixelated lighting device. The pixelated lighting device comprises a plurality of individually addressable light segments (11-19) and the original light settings are associated with respective ones of the individually addressable light segments. The system is further configured to obtain a further original light setting (89) for the further light source, adjust the original light settings based on the further original light setting and the relative location of the further light source, and control the individually addressable light segments to emit light according to the adjusted original light settings.

A system for controlling a pixelated lighting device (1) is configured to determine a location of a further light source (29) relative to the pixelated lighting device and obtain original light settings (91-99) for the pixelated lighting device. The pixelated lighting device comprises a plurality of individually addressable light segments (11-19) and the original light settings are associated with respective ones of the individually addressable light segments. The system is further configured to obtain a further original light setting (89) for the further light source, adjust the original light settings based on the further original light setting and the relative location of the further light source, and control the individually addressable light segments to emit light according to the adjusted original light settings.

An illumination control system is configured for operative association with a vehicle lighting system of an emergency vehicle, such as a fire truck or other first responder type of vehicle. The illumination control system includes a video analysis module configured for receiving data from one or more cameras positioned on an exterior of the vehicle, and each camera has an associated region of interest (ROI) defined for a field-of-view for the camera. An artificial intelligence (AI) module is provided to detect whether a person or object of interest has entered the ROI of the camera. The control system includes an algorithm processing module programmed for executing logic associated with one or more decision-making tasks in association with the operation of the AI module. Also, a light control module can be provided for communicating instructions for activating or deactivating various scene lights of the vehicle lighting system.

An illumination control system is configured for operative association with a vehicle lighting system of an emergency vehicle, such as a fire truck or other first responder type of vehicle. The illumination control system includes a video analysis module configured for receiving data from one or more cameras positioned on an exterior of the vehicle, and each camera has an associated region of interest (ROI) defined for a field-of-view for the camera. An artificial intelligence (AI) module is provided to detect whether a person or object of interest has entered the ROI of the camera. The control system includes an algorithm processing module programmed for executing logic associated with one or more decision-making tasks in association with the operation of the AI module. Also, a light control module can be provided for communicating instructions for activating or deactivating various scene lights of the vehicle lighting system.

An apparatus for controlling an in-vehicle lighting environment includes: a passenger state determination unit that determines a state of a passenger using a gaze of the passenger photographed by a camera of a vehicle; a driving state determination unit that determines a driving state of the vehicle using an acceleration value measured by an acceleration sensor of the vehicle; an external environment state determination unit that determines an external environment state of the vehicle using an external illuminance value measured by an external illuminance sensor of the vehicle; and a lighting environment control unit that controls an illuminance and a color of a first light disposed inside the vehicle based on data determined by at least one determination unit among the passenger state, driving state, and external environment state determination units.

An apparatus for controlling an in-vehicle lighting environment includes: a passenger state determination unit that determines a state of a passenger using a gaze of the passenger photographed by a camera of a vehicle; a driving state determination unit that determines a driving state of the vehicle using an acceleration value measured by an acceleration sensor of the vehicle; an external environment state determination unit that determines an external environment state of the vehicle using an external illuminance value measured by an external illuminance sensor of the vehicle; and a lighting environment control unit that controls an illuminance and a color of a first light disposed inside the vehicle based on data determined by at least one determination unit among the passenger state, driving state, and external environment state determination units.

Devices, systems, and methods for operating a building management system using a lighting control interface are described herein. One device includes an occupancy sensing component, a lighting control interface configured to connect the occupancy sensing device to a lighting control channel of a building, and a building management system (BMS) interface configured to connect the occupancy sensing device to a BMS channel of the building

Devices, systems, and methods for operating a building management system using a lighting control interface are described herein. One device includes an occupancy sensing component, a lighting control interface configured to connect the occupancy sensing device to a lighting control channel of a building, and a building management system (BMS) interface configured to connect the occupancy sensing device to a BMS channel of the building

In various examples, high beam control for vehicles may be automated using a deep neural network (DNN) that processes sensor data received from vehicle sensors. The DNN may process the sensor data to output pixel-level semantic segmentation masks in order to differentiate actionable objects (e.g., vehicles with front or back lights lit, bicyclists, or pedestrians) from other objects (e.g., parked vehicles). Resulting segmentation masks output by the DNN(s), when combined with one or more post processing steps, may be used to generate masks for automated high beam on/off activation and/or dimming or shading—thereby providing additional illumination of an environment for the driver while controlling downstream effects of high beam glare for active vehicles.

In various examples, high beam control for vehicles may be automated using a deep neural network (DNN) that processes sensor data received from vehicle sensors. The DNN may process the sensor data to output pixel-level semantic segmentation masks in order to differentiate actionable objects (e.g., vehicles with front or back lights lit, bicyclists, or pedestrians) from other objects (e.g., parked vehicles). Resulting segmentation masks output by the DNN(s), when combined with one or more post processing steps, may be used to generate masks for automated high beam on/off activation and/or dimming or shading—thereby providing additional illumination of an environment for the driver while controlling downstream effects of high beam glare for active vehicles.