A computer implemented method for operating a wiper system of a sensor enclosure. The wiper system can be configured to receive a camera operating information from one or more cameras. The one or more cameras can be determined to be in an exposure mode based on the camera operating information. A first speed of one or more wipers can be adjusted such that the one or more wipers are not in field of views of the one or more cameras while the one or more cameras are in the exposure mode.

A computer implemented method for operating a wiper system of a sensor enclosure. The wiper system can be configured to receive a camera operating information from one or more cameras. The one or more cameras can be determined to be in an exposure mode based on the camera operating information. A first speed of one or more wipers can be adjusted such that the one or more wipers are not in field of views of the one or more cameras while the one or more cameras are in the exposure mode.

A surroundings monitoring apparatus includes an image acquisition portion acquiring captured image data, an image conversion portion converting the captured image data to virtual image data with a plurality of virtual viewpoints, and a data control portion sequentially displaying the virtual image data at a display unit in a state where each of the virtual viewpoints moves from a first virtual viewpoint position at which an opposite side of the vehicle is viewed, through a second virtual viewpoint position at which the vehicle is viewed from an overhead region of the vehicle, and to a third virtual viewpoint position at which the one side of the vehicle is viewed. The data control portion rotates the virtual image data with reference to a line of sight of the virtual viewpoint while the virtual image data is displayed so that the virtual viewpoint passes through the second virtual viewpoint position.

An apparatus includes a camera, a sensor and a processor. The camera may generate a video signal based on a targeted view of a driver. The sensor may generate a proximity signal in response to detecting an object within a predetermined radius. The processor may determine a location of the object with respect to the vehicle, determine a current location of eyes of the driver, determine a field of view of the driver at a time when the proximity signal is received based on the current location of the eyes, determine whether the object is within the field of view using the current location of the eyes, and generate a control signal. The distance may be determined based on a comparison of reference pixels of a vehicle component in a reference video frame to current pixels of the vehicle component in the video frames.

Systems and methods for automatically self-correcting or correcting in real-time one or more neural networks after detecting a triggering event, or breaching boundary conditions are provided. Such a triggering event may indicate incorrect output signal or data being generated by the one or more neural networks. In particular, machine controllers of the invention limit the operations of neural networks to be within boundary conditions. Autonomous machines of the invention can be self-corrected after a breach of a boundary condition is detected. Autonomous land vehicles of the invention are capable of determining the timing of automatic transition to the manual control from automated driving mode. The controller of the invention filters and saves input-output data sets that fall within boundary conditions for later training of neural networks. The controllers of the invention include security architectures to prevent damages from virus attacks or system malfunctions.

A data-fusion system that fuses lidar-data and camera-data for an automated vehicle includes a camera, a lidar, and a controller. The camera renders an image of an object proximate to a host-vehicle. The lidar detects a distance and a direction to the object based on a reflected-signal of light reflected by the object. The controller is in communication with the camera and the lidar. The controller is configured to determine a reflectivity-characteristic of the object based on the image and the reflected-signal, and adjust a detection-characteristic of the lidar when the reflectivity-characteristic of the object makes it difficult for the lidar to detect the distance and the direction to the object.

A panoramic view system for a vehicle includes at least one real camera (2) for capturing an image of the surroundings of the vehicle from non-centered position, a virtual camera (7), an image processing unit, and a display unit which is configured to represent, in a projected manner, the image captured by the real camera (2) and at least one geometric form (11 to 13) as an overlay over the captured image. The image processing unit is configured to project the captured image onto a first plane (9) perpendicular to the real camera (2) such that perspective distortions in the captured image resulting from the non-centered position of the real camera (2) are distortion-corrected, and to project the at least one geometric form (11 to 13) onto a second plane (10) perpendicular to the virtual camera (7) such that the at least one geometric form (11 to 13) is represented in an undistorted manner on the display unit (4). The image processing unit is configured to find an affine transformation (T) between the first plane (9) and the second plane (10) by a delta transformation between the real camera (2) and the virtual camera (7), and to apply the affine transformation (T) to the first plane (9) which contains the projected and distortion-corrected captured image (8) such that a modified representation (8) of the distortion-corrected captured image (8) is aligned with the at least one undistorted geometric form (11 to 13) in the second plane (10).

A surround view system for a vehicle includes a processor (10) and cameras (2 to 5) that can be arranged on the vehicle so that the cameras (2 to 5) can record images of an outside environment of the vehicle. The processor is configured to analyze a position () and/or a movement of a movable part (14) of the vehicle (1) and to generate a composite image of the outside environment from individual images recorded by the cameras. Furthermore, the processor is configured to calculate an image processing region within the composite image for an adaptive image processing, to determine, based on the analyzed position and/or movement of the movable part (14), that a back-projection of the movable part (14) goes beyond the image processing region, and to modify the image processing region so that the back-projection of the movable part (14) is within the modified image processing region.

Systems and methods to provide visual references to passengers in vehicles to prevent motion sickness. The system can include a controller and one or more projectors and/or displays. The controller can detect movement of a vehicle and project images within the vehicle that comport with the detected movement. The system can include a projector to project images on the interior of the vehicle. The system can include one or more displays to display images inside the vehicle. The controller can receive data from one or more cameras, accelerometers, navigation units, magnetometers, and other components to detect the motion of the vehicle. The system can display visual references on the dashboard, door panels, and other interior surfaces to complete the view of passengers, or provide other visual reference, to prevent motion sickness.

In a vehicle communication system of vehicles, vehicles share environment data such as their location data and 360 degree view of the world with other vehicles using direct vehicle-to-vehicle (V2V) real-time data streams. A displayable map of potentially dangerous obstructions on the surrounding roadway is formed using in vehicle environment sensors allowing a driver or the controls of a driverless vehicle to be warned of the danger. A map of blind spots is built up to speed up the processing of incoming data in order to create a more complete picture of surrounding vehicles. Shared data is used to position each vehicle relative to the target vehicle. By sharing obstruction maps between vehicles, a more complete picture of the roadway can be displayedand one vehicle can in effect see through another vehicle or an obstruction.