Intel Corporation

Harman Becker Automotive Systems Gmbh

Kay-Ulrich Scholl

Koba Natroshvili

Johannes Quast

Bernd Gassmann

Cornelius Buerkle

Andreas Stiegler

Julio Jarquin Arroyo

Fabio Cesari

Heiko Oehring

Ignacio Javier Alvarez

Fabian Oboril

Florian Geissler

Klaus Huebner

Klaus Hübner

Bernd Gaßmann

A mechanism is described for facilitating depth and motion estimation in machine learning environments, according to one embodiment. A method of embodiments, as described herein, includes receiving a frame associated with a scene captured by one or more cameras of a computing device; processing the frame using a deep recurrent neural network architecture, wherein processing includes simultaneously predicating values associated with multiple loss functions corresponding to the frame; and estimating depth and motion based the predicted values.

Depth and motion estimations in machine learning environments

Aspects concern a method for controlling a braking of a vehicle. The method including detecting a braking situation, determining a classification of the braking situation, selecting a braking profile based on the determined classification, and applying a deceleration based on the selected braking profile to maintain a safety distance based on the selected braking profile.

System and method for emergency braking

A training device may include one or more processors configured to generate, using a data augmentation model, augmented sensor data for sensor data, the sensor data provided by a plurality of sensors, wherein the augmented sensor data comprise error states of one or more sensors of the plurality of sensors providing the sensor data, and to train an object detection model based on the augmented sensor data.

Device and method for training an object detection model

Disclosed in some examples are methods, systems, and machine readable mediums which provide for controlled access of vehicle information by trusted authority systems. These systems may allow for police and other authority figures to utilize the onboard systems of the vehicle or obtain other information about the vehicle and occupants while safely in their own vehicles prior to an initial encounter with the vehicle and occupants.

Authorized access to vehicle data

Various aspects of this disclosure provide an area occupancy determining device. The device may include a memory configured to store at least one occupancy grid of a predetermined region, and a processor. The processor may be configured to generate the occupancy grid of the predetermined region. The occupancy grid includes a plurality of grid cells, each grid cell framed by respective grid cell frame lines. At least some of the grid cells have been assigned an information about the occupancy of the region represented by the respective grid cell. The processor may further be configured to dynamically update the occupancy grid, thereby successively generating a plurality of updated occupancy grids. Each updated occupancy grid is moved relative to the previous occupancy grid such that an origin coordinate of the updated occupancy grid is positioned on a contact point of grid cell frame lines of adjacent grid cells.

Area occupancy determining device

According to various examples, a vehicle controller is described comprising a determiner configured to determine information about surroundings of a vehicle, the information about the surroundings comprising information about velocities of objects in the surroundings of the vehicle and a velocity controller configured to input the information about the surroundings of the vehicle and a specification of a path of the vehicle to a convolutional neural network, to determine a target velocity of the vehicle along the path based on an output of the convolutional neural network and to control the vehicle according to the determined target velocity.

Vehicle controller and method for controlling a vehicle

An apparatus for applying a convolutional neural network (CNN) to a wide-angle camera image is described herein. The apparatus includes a camera, controller, convolution mechanism and a fully connected layer. The camera is to capture a wide-angle image, and the controller is to map the image on a 3D surface. The convolution mechanism is to perform convolution on the 3D surface and the fully connected layer is to classify a plurality of features generated by the convolution mechanism.

Convolutional neural network for wide-angle camera images

An occupancy grid object determining device is provided, which may include a grid generator configured to generate an occupancy grid of a predetermined region, the occupancy grid including a plurality of grid cells and at least some of the grid cells having been assigned an information about the occupancy of the region represented by the respective grid cell, a determiner configured to determine at least one object in the occupancy grid wherein the at least one object includes a plurality of grid cells, and a remover configured to remove occupancy information from at least one grid cell of the plurality of grid cells of the determined object.

Occupancy grid object determining devices

A visual odometry device, including: an image sensor configured to provide a first image and a second image; a visual feature extractor configured to extract at least three visual features corresponding to each of the first image and the second image; and a position determiner, configured to determine a change of a position of the at least three visual features between the first image and the second image, and to determine a degree of translation of the visual odometry device based on the determined change of position.

Visual odometry device for 3D vehicle motion estimation and visual odometry method for 3D vehicle motion estimation

Technologies for utilizing a bowl-shaped image include a computing device to receive a first fisheye image capturing a first scene and a second fisheye image capturing a second scene overlapping with the first scene at an overlapping region. The computing device generates a combined image of the first fisheye image and the second fisheye image, performs object classification on a region of interest of at least one of the first fisheye image or the second fisheye image to classify an object within the region of interest, and generates a portion of a bowl-shaped image based on the combined image.

Bowl-shaped imaging system

An extrinsic camera calibration system (ECCS) comprising a surround view system (SVS) interface connecting the ECCS to the SVS. The SVS comprises four cameras mounted to front, back, left, and right sides of a vehicle respectively that are front, back, left, and right cameras, as well as an imaging interface at which images from the four cameras are output. A calibration pattern that comprises a set of features is used by an extrinsic calibration processor. A network interface is connected to the four cameras via the imaging interface. The processor receives, from each camera, an image comprising captured features from the calibration pattern. It determines and stores extrinsic calibration parameters (ECPs) for each camera that map coordinates of the features to camera coordinates and that are usable for subsequent normal operation of the cameras, the ECPs being determined from the image features.

Extrinsic calibration of camera systems

Technologies for visualizing moving objects on a bowl-shaped image include a computing device to receive a first fisheye image generated by a first fisheye camera and capturing a first scene and a second fisheye image generated by a second fisheye camera and capturing a second scene overlapping with the first scene at an overlapping region. The computing device identifies a moving object in the overlapping region and modifies a projected overlapping image region to visualize the identified moving object on a virtual bowl-shaped projection surface. The projected overlapping image region is projected on the virtual bowl-shaped projection surface and corresponds with the overlapping region captured in the first and second fisheye images.

Object visualization in bowl-shaped imaging systems

Technologies for determining a distance of an object from a vehicle include a computing device to identify an object captured in a fisheye image generated by a fisheye camera of the vehicle. The computing device projects a contour of the identified object on a selected virtual plane that is located outside the vehicle and selected from a predefined set of virtual planes based on a location of the identified object relative to the vehicle. The computing device identifies a bottom of the projected contour on the selected virtual plane and determines an intersection point of an imaginary line with a ground plane coincident with a plane on which the vehicle is positioned. The imaginary line passes through each of the identified bottom of the projected contour and the fisheye camera. The computing device determines a location of the identified object relative to the vehicle based on the determined intersection point and the identified bottom of the projected contour.

Tracking objects in bowl-shaped imaging systems

A method for flexibly sintering rare earth permanent magnetic alloy comprises: (1) weighing fine powder of rare earth permanent magnetic alloy, loading the fine powder in molds, and orientedly compacting the fine powder in a press machine and in inert atmosphere to obtain blanks and loading the blanks into charging boxes; (2) after air between the second conveying vehicle and the first isolating valve of the glove box is replaced with inert gas, opening the two isolating valves connected with each other; wherein after a first rolling wheel transmission in the second conveying vehicle transfers the charging tray into the first chamber of the glove box, the two isolating valves are closed, and the second conveying vehicle leaves; (3) after a first conveying vehicle is coupled with a third isolating valve at an end of the second chamber, locking two matching flanges of the two isolating valves tightly; (4) after the first conveying vehicle is coupled with an isolating valve of a sintering furnace, locking matching flanges tightly; and (5) after the sintering furnace is evacuated to a vacuum degree more than 50 Pa, or the sintering furnace is filled with protective gas, processing the blanks with heating and heat preservation according to a preset process curve; wherein the blanks are sintered at a highest temperature of 1200&#xb0; C. The present invention significantly increases performance of magnets.

Growing community of inventors

Karlsbad, Germany

Kay-Ulrich Scholl