Abstract: A method and apparatus for annotating point cloud data. An apparatus may be configured to cause display of the point cloud data, label points in the point cloud data with a plurality of annotation points, the plurality of annotation points corresponding to points on a human body, move, in response to a user input, one or more of the annotation points to define a human pose and create annotated point cloud data, and output the annotated point cloud data.

Abstract: The present invention provides an on-board vehicle system and method for camera or sensor-based parking spot detection and identification. Advantageously, this system and method utilizes a standard front (or side or rear) camera or sensor image to detect and identify one or more parking spots at a distance via vector or like representation using a deep neural network trained with data annotated using an annotation tool, without first transforming the standard camera or sensor image(s) to a bird's-eye-view (BEV) or the like. The system and method can be incorporated in a driver-assist (DA) or autonomous driving (AD) system.

Abstract: A method, software tool, and system for generating realistic synthetic ride-requests associated with a mobility or transportation service, including: utilizing a generative adversarial network, learning the spatial-temporal distribution of a plurality of real ride-requests; and, utilizing the generative adversarial network and based on the learning step, generating one or more synthetic source and destination ride-request geolocations that retain a statistical distribution of the plurality of real ride-requests. The generative adversarial network is a Wasserstein generative adversarial network.

Abstract: An apparatus and method for estimating the pose of one or more persons is described. An example technique may include receiving a point cloud from a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor, processing the point cloud to produce a voxelized frame including a plurality of voxels, processing the voxelized frame using a deep neural network to determine one or more persons relative to the LiDAR sensor and a pose for each of the one or more persons, and outputting a location of the determined one or more persons and the pose for each of the determined one or more persons.

Abstract: A method performed by a vehicle system for handling images of the surroundings of a vehicle. An image of the surroundings of the vehicle is obtained. The image is obtained from at least one side image capturing device mounted in or on the vehicle, and the image capturing device comprises a fisheye camera lens. At least a part of the distortions in the image is corrected to obtain a corrected image. The corrected image is rotationally transformed using a first rotational transformation to obtain a first transformed image. The corrected image is rotationally transformed using a second rotational transformation to obtain a second transformed image. The first and second transformed images are consecutive or adjacent images.

Abstract: A parking availability map system and method, including: a sensor-based parking space detection system coupled to a vehicle and operable for detecting and identifying open and occupied parking spaces within a predetermined vicinity of the vehicle; and an application server disposed remotely from the vehicle and executing an algorithm operable for receiving open and occupied parking space data from the sensor-based parking space detection system, receiving a parking space availability query from a user disposed remotely from the server, and, responsive to the parking space availability query, transmitting parking space availability information to the user. Optionally, the sensor-based parking space detection system includes a camera-based parking space detection system.

Abstract: Parking spot detection methods and systems that detect and pair parking line markers, improving the accuracy and speed of parking spot detection in vehicular driver assist (DA) and autonomous driving (AD) applications. These methods and systems incorporate three major steps: (1) preprocessing—one or more standard camera images are input into the algorithm and a bird's-eye-view (BEV) image is output from the algorithm; (2) deep neural network (DNN) segmentation of the parking line markers—the BEV image is input into the algorithm and a binary image is output from the algorithm (with, e.g., the parking line markers areas displayed in white and the background displayed in black); and (3) binary image processing—the binary image is input into the algorithm and detected and paired parking line markers representing the parking spots are output from the algorithm.

Abstract: The present invention provides an on-board vehicle system and method for camera or sensor-based parking spot detection and identification. Advantageously, this system and method utilizes a standard front (or side or rear) camera or sensor image to detect and identify one or more parking spots at a distance via vector or like representation using a deep neural network trained with data annotated using an annotation tool, without first transforming the standard camera or sensor image(s) to a bird's-eye-view (BEV) or the like. The system and method can be incorporated in a driver-assist (DA) or autonomous driving (AD) system.

Abstract: The present invention relates to a driver assistance (DA) system and method for vehicle flank safety. This system and method also have utility in autonomous driving (AD) applications. The system and method optionally pair one or more cameras with one or more proximity sensors to provide free space awareness and contact avoidance related to the flank of a vehicle, providing a virtual distance grid overlay on one or more camera views available to an operator, predefined safety boundary intrusion detection under low-speed maneuvering conditions, door-open obstruction and danger detection, and a flank illumination system.

Abstract: Methods and systems for digital image segmentation and annotation, including: receiving a digital image depicting, in part, an object of interest from an input file; one or more of manually and automatically adding a polygon around the object of interest to generate a segmented digital image; one or more of manually and automatically appending a label to the polygon around the object of interest to generate a segmented and annotated digital image, wherein the label indicates one or more of an identity and a characteristic of the object of interest; and outputting information related to the segmented and annotated digital image to an output file. Optionally, the polygon is one of a bounding box and a 4-point polygon. Optionally, the object of interest is a parking spot.

Abstract: A method performed by a vehicle system for handling images of surroundings of a vehicle. An image of surroundings of the vehicle is obtained. The image is obtained from at least one image capturing device mounted in or on the vehicle, and the image capturing device comprises a fisheye lens. At least a part of distortions in the image is corrected to obtain a corrected image. The corrected image is rotationally transformed using a first rotational transformation to obtain a first transformed image. The corrected image is rotationally transformed using a second rotational transformation to obtain a second transformed image. The first and second rotational transformations are different from each other, and the first and second transformed images are consecutive images.