A computer-assisted method for analyzing person-related accident data of one or more vehicle occupants, the person-related accident data including at least image data of a video sequence of the vehicle occupants during an accident. The computer-assisted method includes at least one first step of detecting a pattern on the basis of the image data, and at least one second step of comparing the detected pattern with a number of previously stored patterns.

A fingerprint detection apparatus includes a plurality of fingerprint detection units distributed in an array or disposed alternately, and each of the plurality of fingerprint detection units includes: one micro-lens configured to be disposed under the display screen; a plurality of optical sensing pixels, a center position of a photosensitive region of each of the plurality of optical sensing pixels being offset relative to a center position of the same optical sensing pixel; and at least one light shielding layer disposed between the one micro-lens and the plurality of optical sensing pixels, each of the at least one light shielding layer being provided with a hole corresponding to the plurality of optical sensing pixels. By offsetting photosensitive regions of the optical sensing pixels, the thickness of an optical fingerprint module can be reduced on the basis of improving a fingerprint identification effect on a dry finger.

Provided herein is a system and method that acquires data and determines a driving action based on the data. The system comprises a processor configured to acquire data of nonuniform resolution over a field of view of the sensor, and a controller configured to determine a driving action of a vehicle based on the data, and perform the driving action.

An artificial window is provided. The artificial window includes a window having a frame, and a display device disposed at a back side of the window. The display device is used to display images. An image capturing device may be disposed on the window to obtain a viewer image of a viewer. Thus, a viewing direction of the viewer may be determined based on the viewer image, and the images being displayed on the display device may be changed based on the location of the viewer. The display device may be a curved display device, which is wider than the frame. Alternatively, the display device may be a flat display device, and a lenticular array formed by lenticular lenses is disposed between the window and the flat display device. Each of the lenticular lenses is focused on or close to a display surface of the flat display device.

Systems, methods and devices are for optical imaging are described. A system includes a light source and a light detection unit. The light source includes a light-emitting device and a first spectral filter opposite the light emitting device. The first spectral filter includes at least one dielectric filter and has a first angular dependence of a transmission passband. The light source further includes at least one reflector adjacent side surfaces of the light emitting device. The light detection unit includes an optical sensor and a second spectral filter opposite the optical sensor. The second spatial filter has a second angular dependence of a transmission passband that is matched to the first angular dependence.

In various examples, live perception from wide-view sensors may be leveraged to detect features in an environment of a vehicle. Sensor data generated by the sensors may be adjusted to represent a virtual field of view different from an actual field of view of the sensor, and the sensor data—with or without virtual adjustment—may be applied to a stereographic projection algorithm to generate a projected image. The projected image may then be applied to a machine learning model—such as a deep neural network (DNN)—to detect and/or classify features or objects represented therein. In some examples, the machine learning model may be pre-trained on training sensor data generated by a sensor having a field of view less than the wide-view sensor such that the virtual adjustment and/or projection algorithm may update the sensor data to be suitable for accurate processing by the pre-trained machine learning model.

A detection system includes a filter and a detector. The filter receives input packets from one or more dynamic vision sensors (DVSs) deployed in an area. The filter reduces the bandwidth of the input packets to produce compressed data packets while maintaining good end-to-end detection capability. The compressed data packets are transmitted to a detector in a low bandwidth transmission. The detector may collect compressed data packets from many filters. The detector determines if an object of interest is represented in the information of the input packets. If an object of interest is present, the detector activates an alarm, sends a notification to a person associated with the area and/or stores an indication in a database.

A camera is module mounted on an inside of a windshield in a vehicle and is configured to photograph an outside of the vehicle. The camera module includes: a first camera for photographing the outside with a first imaging element through a first lens unit; and a second camera having a view angle narrower than the first camera and photographing the outside with a second imaging element through a second lens unit. The first imaging element and the second imaging element have a same pixel specification.

Systems, methods and devices are for optical imaging are described. A system includes a light source and a light detection unit. The light source includes a light-emitting device and a first spectral filter opposite the light emitting device. The first spectral filter includes at least one dielectric filter and has a first angular dependence of a transmission passband. The light source further includes at least one reflector adjacent side surfaces of the light emitting device. The light detection unit includes an optical sensor and a second spectral filter opposite the optical sensor. The second spatial filter has a second angular dependence of a transmission passband that is matched to the first angular dependence.

A movable carrier auxiliary system includes a driver state detecting device and a control device. The driver state detecting device includes a biometric feature detecting module, a storage module, and an operation module. The biometric feature detecting module detects a biometric feature of a driver. The storage module stores a first biometric feature parameter, a second biometric feature parameter, a first operating mode corresponding to the first biometric feature parameter, and a second operating mode corresponding to the second biometric feature parameter. The operation module detects whether the biometric feature of the driver matches with the first biometric feature parameter or the second biometric feature parameter or not via the biometric feature detecting module, and to correspondingly generate a detection signal. The control device retrieve the first operating mode or the second biometric feature parameter from the storage module to control the movable carrier based on the detection signal.