Volvo Car Corporation

Autoliv Development Ab

Ford Global Technolgoies, LLC

Polestar Performance Ab

Andreas Wallin

Sihao Ding

Sohini Roy Chowdhury

Yann Le Merrer

Yannick Erb

Peter Hardå

Minming Zhao

Ying Li

Harshavardhan Reddy Dasari

Megha Maheshwari

Saudin Botonjic

Sylvain Triquigneaux

Jörgen Persson

Gambhir Bista

Fredrik Tidborg

Yann Lemerrier

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.

Driver assistance system and method for vehicle flank safety

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.

Tool and method for annotating a human pose in 3D point cloud data

An on-board vehicle system and method for camera or sensor-based parking spot detection and identification is provided. 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.

System and method for camera or sensor-based parking spot detection and identification

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.

Methods and systems for semi-automated image segmentation and annotation

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.

Vehicle lane marking and other object detection using side fisheye cameras and three-fold de-warping

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.

Lidar-based multi-person pose estimation

There is provided a method for image segmentation and annotation. The method comprises: providing an image; reducing the image size and smoothing the image to perform low-pass filtering on the image; restoring the image size; performing pixel level clustering of the image; performing spatial clustering of the image; selecting one or more clusters corresponding to a region of interest, ROI, based on a predetermined image mask, wherein the predetermined image mask has a known location in relation to the ROI; defining the selected one or more clusters as a proposed ROI; and determining if the proposed ROI sufficiently corresponds to an actual ROI.

Method of segmentation and annotation of images

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&#x2014;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&#x2014;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&#x2014;the binary image is input into the algorithm and detected and paired parking line markers representing the parking spots are output from the algorithm.

Methods and systems for parking line marker detection and pairing and parking spot detection and classification

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.

A vehicle safety system comprising: at least one occupant safety device () for protecting an occupant of the vehicle () in the event of a side impact; and a control unit () operable to receive information from one or more vehicle sensors () and to provide a trigger signal to activate the occupant safety device (). Under normal driving conditions, a default deployment algorithm is used by the control unit () to determine whether the trigger signal should be generated; and if it is determined that loss of control of the vehicle () is occurring, or is expected to occur, and the longitudinal speed of the vehicle () exceeds a first threshold, the control unit () employs a first further deployment algorithm to determine whether the trigger signal should be generated. The first further deployment algorithm being adapted to cause the trigger signal to be generated a shorter time after the initiation of a side impact than is the case for the default deployment algorithm.

Vehicle safety system

A vehicle safety system for detecting a side impact, the system comprising at least one sensor operable to detect one or more parameters relating to the rate of yaw of the vehicle, and to provide an output that is dependent upon the longitudinal position along the vehicle of the side impact.

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Billdal, Sweden

Andreas Wallin