Abstract: System and method for adjusting side mirrors capture, by a primary camera, a facial image of a driver; estimate a 3D eye location with respect to a 3D coordinate of the primary camera based on the captured facial image; map the estimated 3D eye location to a respective set of pitch and yaw of the side mirror based on a look up table; and rotate the side mirror to a target position in accordance with the respective set of pitch and yaw. The lookup table includes a plurality of rows, each containing a pair of a key and a set of values. The key is a set of three parameters of 3D line of locations where the driver can observe an optimal view behind the vehicle, and the set of values includes a set of pitch and yaw of the side mirror corresponding to the 3D line of locations.

Abstract: A system for generating an advanced surround view for a vehicle includes a plurality of cameras to generate image data. Sensors are configured to determine kinematic data of the vehicle. Kinematic data are also obtained by a visual odometry module and/or a deep-vision mechanism. A processor is configured to process image data captured by the plurality of cameras to generate a 360-degree surround view layer representative of a surrounding area. The processor is further configured to construct an improved bicycle kinematic model for processing kinematic data to generate an under-chassis layer representative of an area under the vehicle. The processor is further configured to generate a 3D vehicle model layer and overlay objects layer. Display screens display a GUI of combined scene of the 360-degree surround view layer, under-chassis layer, 3D vehicle model layer and overlay objects layer as would be viewed from a virtual camera viewpoint.

Abstract: A method for adjusting a side mirror of a vehicle includes capturing, by a primary camera provided in front of a driver of the vehicle, a facial image of the driver; estimating a 3D eye location with respect to a 3D coordinate of the primary camera based on the captured facial image; mapping the estimated 3D eye location to a respective set of pitch and yaw angles of the side mirror based on a lookup table; and rotating the side mirror to a target position in accordance with the respective set of pitch and yaw angles. The lookup table includes a plurality of rows, each row containing a pair of a key and a set of values. The key is a 3D eye location, and the set of values includes a set of pitch and yaw angles of the side mirror corresponding to the 3D eye location.

Abstract: The present invention relates to a method and a system for training a machine learning model for face recognition. The method comprising generating a training dataset; providing a teacher model that comprises a teacher backbone and a teacher head; iteratively training the teacher backbone and the teacher head using the training dataset; setting the machine learning model to include a lightweight backbone and a lightweight head; copying trained parameters of the trained teacher head to the lightweight head; and iteratively training the lightweight backbone using the training dataset.

Abstract: Maneuver assisting system and method are provided. The system includes at least one processor configured to process operations of the system; one or more memories for storing instructions; a communication unit for communicating between components of the system, and between the system and the vehicle and/or a user equipment; a Surrounding View Monitoring (SVM) unit comprising a plurality of sensors for providing a plurality of vehicle-related information; a Human-Machine Interface (HMI) configured for a driver of the vehicle to interact with the system; a display unit configured to display the HMI; a motion planning unit configured to generate a trajectory for the vehicle to follow; and a motion control unit configured to control the automated maneuvers of the vehicle. In particular, the HMI is configured to be implemented in the user equipment in order for the driver to remotely operate the system using the user equipment.

Abstract: A method and system for training a machine learning model for point of gaze prediction simultaneously analyzes the image of the scene and the image of the driver's face using the machine learning model to generate a point of gaze of a driver in traffic environments. The machine learning model comprises a camera calibration module that can compute an embedding vector that represents the spatial configuration between the driver and the camera system. The camera calibration module improves the overall model's performance, which is jointly trained end to end.

Abstract: A method of clustering using encoder-decoder model based on attention mechanism extracts image features, clusters to form image feature vector clusters, and based on the cosine similarity score between the image feature vectors to arrange each image feature vector cluster into an image feature vector sequence. The image feature vector sequence includes cosine distance encoding vectors concatenated with respective image feature vectors and is used as the input data sequence in encoder and decoder neural network models to generate an output data sequence from the input data sequence. The output data sequence is a binary sequence having values of 1 or 0 at a position denoting that the image corresponding to the position is or is not in the same cluster with respect to the center image of the cluster.

Abstract: A parking slot detection method and system includes receiving a plurality of images taken from a plurality of cameras mounted on a vehicle in a parking environment; generating a top view image comprising a surrounding view of the vehicle based on the plurality of images; processing the top view image using a parking line detection model that has been trained using an annotated dataset to detect parking lines for a parking slot in the parking environment, estimate a bounding box for the parking slot and identify an occupancy state of the parking slot; and converting pixel coordinate information of the bounding box to vehicle information.

Abstract: In a method and apparatus for extreme-light image enhancement, an input image is passed to a lighting classification module in order to be classified into two categories: an under-exposed lighting condition and an over-exposed light condition. Then the classified image is subsequently passed to an enhancement module which comprises a first image enhancement unit and a second image enhancement unit using a mapping function based on a gamma mapping function for mapping the classified image to an output image, respectively. A parameter of the gamma mapping function is estimated by a parameter estimation network, based on the classified image. The parameter estimation network may be a lightweight CNN network which is trained by an unsupervised approach.

Abstract: A driver monitor system includes an image data acquiring module configured to acquire a plurality of image data from a data collection module; a training module configured to train a plurality of teacher models to obtain a plurality of feature groups using the plurality of image data, and transfer a plurality of pieces of knowledge obtained from the plurality of feature groups to a plurality of student models, respectively; and at least one edge device comprising the plurality of student models configured to use a pipeline design pattern with multiple threads to make a warning.