Abstract: 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.

Abstract: A method for classifying a driving behavior of a road user in the environment of an ego vehicle. The method includes: receiving trajectory data of a driving trajectory of a road user arranged in the environment of an ego vehicle; ascertaining a latent-space representation of the driving trajectory of the road user in a latent space; ascertaining a distance of the latent-space representation of the driving trajectory of the road user to a latent-space representation of at least one normal trajectory in the latent space and classifying a driving behavior of the road user as a normal driving behavior if the distance in the latent space falls below a predetermined limit value; or classifying the driving behavior of the road user as an abnormal driving behavior if the distance in the latent space exceeds the predetermined limit value.

Abstract: A method for classifying radar data, which have been obtained by registering radar radiation emitted from a transmitter and reflected by at least one object using at least one detector. The method includes: providing radar data, which include observations of a setting recorded at different points in time; ascertaining at least one portion of the radar data, which is rotated and/or scaled in at least one of the observations as compared to at least one other of the observations; ascertaining a fixed point of the rotation and/or scaling; transforming at least one two-dimensional representation of at least one part of the observations into logarithmic polar coordinates using the ascertained fixed point as the origin; and mapping the at least one transformed two-dimensional representation onto at least one class of a predefined classification via at least one classifier, which encompasses a neural network that includes at least one convolution layer.

Abstract: A method for classifying a driving behavior of a road user in the environment of an ego vehicle. The method includes: receiving trajectory data of a driving trajectory of a road user arranged in the environment of an ego vehicle; ascertaining a latent-space representation of the driving trajectory of the road user in a latent space; ascertaining a distance of the latent-space representation of the driving trajectory of the road user to a latent-space representation of at least one normal trajectory in the latent space; and classifying a driving behavior of the road user as a normal driving behavior if the distance in the latent space falls below a predetermined limit value; or classifying the driving behavior of the road user as an abnormal driving behavior if the distance in the latent space exceeds the predetermined limit value.

Abstract: 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.

Abstract: A method and device for operating an automated vehicle at a traffic intersection.

Abstract: Autonomous unmanned vehicles (UVs) for responding to situations are described. Embodiments include UVs that launch upon detection of a situation, operate in the area of the situation, and collect and send information about the situation. The UVs may launch from a vehicle involved in the situation, a vehicle responding to the situation, or from a fixed station. In other embodiments, the UVs also provide communications relays to the situation and may facilitate access to the situation by responders. The UVs further may act as decoupled sensors for vehicles. In still other embodiments, the collected information may be used to recreate the situation as it happened.

Abstract: 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.

Abstract: 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.

Abstract: A method for classifying radar data, which have been obtained by registering radar radiation emitted from a transmitter and reflected by at least one object using at least one detector. The method includes: providing radar data, which include observations of a setting recorded at different points in time; ascertaining at least one portion of the radar data, which is rotated and/or scaled in at least one of the observations as compared to at least one other of the observations; ascertaining a fixed point of the rotation and/or scaling; transforming at least one two-dimensional representation of at least one part of the observations into logarithmic polar coordinates using the ascertained fixed point as the origin; and mapping the at least one transformed two-dimensional representation onto at least one class of a predefined classification via at least one classifier, which encompasses a neural network that includes at least one convolution layer.

Abstract: 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.

Abstract: 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.

Abstract: A perception device, including at least one image sensor configured to detect a plurality of images; an information estimator configured to estimate from each image of the plurality of images a depth estimate, a velocity estimate, an object classification estimate and an odometry estimate; a particle generator configured to generate a plurality of particles, wherein each particle of the plurality of particles comprises a position value determined from the depth estimate, a velocity value determined from the velocity estimate and a classification value determined from the classification estimate; an occupancy hypothesis determiner configured to determine an occupancy hypothesis of a predetermined region, wherein each particle of the plurality of particles contributes to the determination of the occupancy hypothesis.

Abstract: A method for sensing the position of a vehicle on a three-dimensional map is provided, which may include receiving, from one or more sensors, sensor information about a three-dimensional position of the vehicle; and applying a distributed Kalman filter to the sensor information to determine the position of the vehicle on the three-dimensional map.

Abstract: Autonomous unmanned vehicles (UVs) for responding to situations are described. Embodiments include UVs that launch upon detection of a situation, operate in the area of the situation, and collect and send information about the situation. The UVs may launch from a vehicle involved in the situation, a vehicle responding to the situation, or from a fixed station. In other embodiments, the UVs also provide communications relays to the situation and may facilitate access to the situation by responders. The UVs further may act as decoupled sensors for vehicles. In still other embodiments, the collected information may be used to recreate the situation as it happened.

Abstract: A perception device including a receiver configured to receive sensor information including information about a location of one or more objects detected by a sensor; a memory configured to store an occupancy grid of a predetermined region, wherein the occupancy grid includes a plurality of grid cells, wherein each grid cell represents an area in the predetermined region, wherein at least one grid cell of the plurality of grid cells is associated with a respective single occupancy hypothesis; a single occupancy hypothesis determiner configured to determine a single occupancy hypothesis; wherein the single occupancy hypothesis includes degree of belief of occupancy of the grid cell depending on the sensor information; wherein a contribution of a sensor information value to the degree of belief of occupancy substantially decreases with an increase of a distance of the location of the object detected by the sensor from a center of the grid cell.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.