A vehicle headlight has a segmented light guide device in which segments are selectively controlled to operate in an output state that reduces glare to oncoming vehicle drivers. Vehicle sensors and controller circuitry are used to identify areas of glare removal in the light pattern of the headlight and control the corresponding segments in the light guide device to operate in a selected light output state that reduces glare. The segments can be operated in a focused output state for vehicle driver visibility, or in a scattered output state to reduce glare. The segments can be operated in a clear or transparent output state for vehicle driver visibility, or in a reflected output state to reduce glare. Reflected light can optionally be re-directed. The light guide device can employ bi-stable liquid crystal shuttering or liquid crystal switchable mirror technology.

The present invention relates to a lighting system for a motor vehicle. The lighting system comprises at least one first light emitting device mounted on a front right side of the vehicle and adapted to project in front of the vehicle a first pixelated light beam. The lighting system further comprises at least one second light emitting device mounted on a front left side of the vehicle and adapted to project in front of the vehicle a second pixelated light beam. One of the first pixelated light beam and second pixelated light beam, called high resolution beam, has a number of pixels greater than or equal to at least five times, in particular greater than or equal to at least ten times, the number of pixels of the other of said pixelated light beams, called low resolution beam.

A vehicular control system includes a forward viewing camera disposed at an in-cabin side of a windshield of a vehicle and viewing forward of the vehicle. Road curvature of a road along which the vehicle is traveling is determined responsive at least in part to processing by an image processor of image data captured by the camera. Responsive at least in part to processing of captured image data, a traffic lane of the road along which the vehicle is traveling is determined. Upon approach of the vehicle to a curve in the road, speed of the vehicle is reduced to a reduced speed for traveling around the curve in the road at least in part responsive to at least one selected from the group consisting of (a) processing of image data captured by the forward viewing camera and (b) data relevant to a current geographical location of the equipped vehicle.

A vehicle-mounted equipment control device includes a controller configured to cause equipment of any of a headlight, a wiper, and an air-conditioning device to continue a visual securing operation for securing visibility of a driver of a vehicle even after driving control of the vehicle is switched from automated driving control to manual driving control regardless of an operation mode of the equipment specified by an operation switch for setting an operation of the equipment when an operation mode executed by the equipment immediately before driving control of the vehicle is switched from automated driving control to manual driving control is an operation mode corresponding to the visual securing operation.

A system for simulation of a composite beam is disclosed. The system can comprise a memory storing executable instructions and one or more processors coupled to the memory to execute the executable instructions. The one or more processors can be configured to generate a representation of the original beam pattern transmitted via a propagation of the composite beam, to invoke a propagation model that represents a distortion for the propagation of the composite beam, and to determine a representation of a distorted beam pattern based on the propagation model and on the representation of the original beam pattern transmitted via the propagation.

A vehicular vision system includes an image processor and a camera that views through the windshield of the vehicle. The camera captures image data as the vehicle travels along a road, and the image processor processes image data captured by the camera. The vehicular vision system, responsive at least in part to processing by the image processor of image data captured by the camera, determines when the vehicle is at a construction zone. Responsive to determining that the vehicle is at the construction zone, the vehicular vision system adjusts a vehicular driver assistance system of the vehicle. The vehicular vision system determines that the vehicle exits the construction zone based at least in part on processing by the image processor of image data captured by the camera.

A vehicular control system includes a plurality of sensors that include at least a camera and a 3D point-cloud LIDAR. As the vehicle travels along a road, and responsive at least in part to processing at an electronic control unit of 3D point-cloud LIDAR data captured by the 3D point-cloud LIDAR, the vehicular control system (a) determines presence of a pedestrian or cross traffic vehicle present exterior of the vehicle that (i) is not on the road that is being travelled along by the vehicle and is approaching the road to cross the road ahead of the vehicle and (ii) is at least in the field of sensing of the 3D point-cloud LIDAR and (b) at least in part controls at least one vehicle function of the vehicle responsive at least in part to the determined presence of the pedestrian or cross traffic vehicle.

In order to provide an enhanced driver assistance system for a vehicle, a prediction of a movement of the third-party vehicle is determined based upon motion data relating to a third- party vehicle travelling in front which has moved out of a region of view of at least one sensor of the vehicle, or relating to an oncoming third-party vehicle which has not yet entered the region of view of the sensor, and based upon map data The driver assistance system is then operated on the basis of the prediction.

A system for implementing adaptive light distributions for an autonomous vehicle (comprises the autonomous vehicle, a control device, and a headlight associated with the autonomous vehicle. The control device receives sensor data from sensors of the autonomous vehicle, where the sensor data comprises an image of one or more objects on a road traveled by the autonomous vehicle. The control device determines that a light condition level on a particular portion of the image is less than a threshold light level. The control device adjusts the headlight to increase illumination on a particular part of the road that is shown in the particular portion of the image.

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.