Volvo Car Corporation

Oracle International Corporation

Ying Li

Sihao Ding

Minming Zhao

Sohini Roy Chowdhury

Zhen Hua Liu

Hui Joe Chang

Andreas Wallin

Usha Nookala

Harshavardhan Reddy Dasari

Cheng Zhang

Saudin Botonjic

Gambhir Bista

Oskar Karnblad

A cross-range 3D object detection method and system operable for training a 3D object detection model with N sub-groups of a point cloud corresponding to N detection distance ranges to form N 3D object detection models forming an ensemble 3D object detection model. Training the 3D object detection model with the N sub-groups of the point cloud corresponding to the N detection distance ranges includes training the 3D object detection model progressively from distant to near. Training the 3D object detection model with the N sub-groups of the point cloud corresponding to the N detection distance ranges includes, each time the 3D object detection model converges, saving resulting weights and adding a corresponding network to the ensemble 3D object detection model.

Ensemble learning for cross-range 3D object detection in driver assist and autonomous driving systems

A LIDAR-based method of determining an absolute speed of an object at a relatively longer distance from an ego vehicle, including: estimating a self speed of the ego vehicle using a first frame t−1 and a second frame t obtained from a LIDAR sensor by estimating an intervening rotation θ about a z axis and translation in orthogonal x and y directions using a deep learning algorithm over a relatively closer distance range; dividing each of the first frame t−1 and the second frame t into multiple adjacent input ranges and estimating a relative speed of the object at the relatively longer distance by subsequently processing each frame using a network, with each input range processed using a corresponding convolutional neural network; and combining the estimation of the estimating the self speed with the estimation of the estimating the relative speed to obtain an estimation of the absolute speed.

Long range lidar-based speed estimation

The present disclosure provides a system and method for removing noise from an ultrasonic signal using a generative adversarial network (GAN). The present disclosure provides three input formats for the neural network (NN) in order to feed one-dimensional (1D) input data to the network. The system is generalizable to multiple noise sources, as it learns from different motion functions and noise types. The end-to-end system of the present disclosure is trained on raw ultrasonic signals with very little pre-processing or feature extraction.

System and method for ultrasonic signal noise removal using a deep neural network

A LIDAR-based method of determining an absolute speed of an object at a relatively longer distance from an ego vehicle, including: estimating a self speed of the ego vehicle using a first frame t-and a second frame t obtained from a LIDAR sensor by estimating an intervening rotation &#x3b8; about a z axis and translation in orthogonal x and y directions using a deep learning algorithm over a relatively closer distance range; dividing each of the first frame t-and the second frame t into multiple adjacent input ranges and estimating a relative speed of the object at the relatively longer distance by subsequently processing each frame using a network, with each input range processed using a corresponding convolutional neural network; and combining the estimation of the estimating the self speed with the estimation of the estimating the relative speed to obtain an estimation of the absolute speed.

Long range LIDAR-based speed estimation

An apparatus and method for performing environment perception is described. An example technique may include receiving a point cloud from a sensor, such as a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor. The example techniques may further include dividing the point cloud into a plurality of distances ranges, processing the points in each distance range of the point cloud with a different section of a plurality of sections of a piece-wise network structure, and outputting environment perception data from the piece-wise network structure.

Piece-wise network structure for long range environment perception

Methods and systems for improved query evaluation in computer systems are provided. A system receives a full text query for evaluation against a collection of XML documents. The query comprises specifies one or more tokens and one or more contexts. The contexts each represent a path within an XML expression. The query is used to make multiple determinations. First, determining, using token lists, first documents that contain the tokens included in the query. Second, determining, using tag lists, second documents that contain the contexts included in the query. Third, determining third documents by comparing the first documents and the second documents to determine which documents contain both the tokens and the contexts. The query is then evaluated in view of the third documents.

Evaluating XML full text search

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.

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Sunnyvale, CA, United States of America

Ying Li