Gm Global Technolgoy Operations, Inc.

Xerox Corporation

Hrl Laboratories, LLC

University of Southern California

Language Weaver, Inc.

Kenji Yamada

Rajan Bhattacharyya

Kevin Knight

Kyungnam Kim

Michael J Daily

Hyukseong Kwon

Nicola Cancedda

Aruna Jammalamadaka

Dmitriy V Korchev

Eric Gaussier

Cyril Goutte

Chong Ding

Michel Simard

Greg Langmead

Marc Dymetman

Daniel Marcu

Jiejun Xu

Jaehoon Choe

Alireza Esna Ashari Esfahani

Tiffany J Hwu

Bruno Cavestro

Arne Mauser

Divya P Kulkarni

Darren Michael Chan

Joshua Lampkins

Alan Perry

A road sign interpretation system includes a front-facing camera mounted on or in a vehicle collecting image data of multiple road signs. A first convolutional neural network (CNN) receives the image data from the front-facing camera and yields a set of sign predictions including one or more sign text instances. A second CNN defining a text extractor receives the image data from the front-facing camera and extracts text candidates including the multiple sign text instances. Sign and sign data localization is provided in the second CNN to compute a text order from the multiple sign text instances. A sign text synthesizer module receives individual sign text instances from the first CNN and individual ones of the sign text instances in digitized forms from an optical character recognizer (OCR). A semantic encoding and interpretation module receives the sign text instances and identifies semantics of the multiple road signs.

Scalable road sign interpretation system for autonomous driving

A vehicle and a system and a method of operating the vehicle. The system includes a reasoning engine, an episodic memory, a resolver and a controller. The reasoning engine infers a plurality of possible scenarios based on a current state of an environment of the vehicle. The episodic memory determines a historical likelihood for each of the plurality of possible scenarios. The resolver selects a scenario from the plurality of possible scenarios using the historical likelihoods. The controller operates the vehicle based on the selected scenario.

System for extending functionality of hypotheses generated by symbolic/logic-based reasoning systems

Described is a system for analyzing time series data. A sequence of symbols is generated from a set of time series input data related to a moving vehicle using automatic segmentation. A grammar is extracted from the sequence of symbols, and the grammar is a subset of a probabilistic context-free grammar (PCFG). Using the grammar, time series input data can be analyzed, and a prediction of the vehicle's movement can be made. Vehicle operations for an autonomous vehicle are determined using the prediction.

Method for time series structure discovery

A control system of the autonomous vehicle may generate multiple possible behavior control movements based on the driving goal and the assessment of the vehicle environment. In doing so, the method and system selects one of the best behavior control, among the multiple possible movements, and the selection is based on the quantitative grading of its driving behavior.

Forward modeling for behavior control of autonomous vehicles

A method of path planning for a host vehicle includes: receiving host vehicle, environmental and obstacle information; calculating one or more projected host vehicle locations; computing a projected obstacle location for each obstacle; and determining a collision potential between each projected host vehicle location and each projected obstacle location. Until a maximum number of steps is reached, and while at least one projected host vehicle location has an associated collision potential below a collision threshold, the method further includes repeating the calculating, computing and determining steps.

System, method and controller for graph-based path planning for a host vehicle

A statistical translation model (TM) may receive a parse tree in a source language as an input and separately output a string in a target language. The TM may perform channel operations on the parse tree using model parameters stored in probability tables. The channel operations may include reordering child nodes, inserting extra words at each node (e.g., NULL words) translating leaf words, and reading off leaf words to generate the string in the target language. The TM may assign a translation probability to the string in the target language.

Syntax-based statistical translation model

Methods are disclosed for performing proper word alignment that satisfy constraints of coverage and transitive closure. Initially, a translation matrix which defines word association measures between source and target words of a corpus of bilingual translations of source and target sentences is computed. Subsequently, in a first method, the association measures in the translation matrix are factorized and orthogonalized to produce cepts for the source and target words, which resulting matrix factors may then be, optionally, multiplied to produce an alignment matrix. In a second method, the association measures in the translation matrix are thresholded, and then closed by transitivity, to produce an alignment matrix, which may then be, optionally, factorized to produce cepts. The resulting cepts or alignment matrices may then be used by any number of natural language applications for identifying words that are properly aligned.

Apparatus and methods for aligning words in bilingual sentences

An adapter for a text to text training. A main corpus is used for training, and a domain specific corpus is used to adapt the main corpus according to the training information in the domain specific corpus. The adaptation is carried out using a technique that may be faster than the main training. The parameter set from the main training is adapted using the domain specific part.

Adapter for allowing both online and offline training of a text to text system

Quality of machine translation of natural language is determined by computing a sequence kernel that provides a measure of similarity between a first sequence of symbols representing a machine translation in a target natural language and a second sequence of symbols representing a reference translation in the target natural language. The measure of similarity takes into account the existence of non-contiguous subsequences shared by the first sequence of symbols and the second sequence of symbols. When the similarity measure does not meet an acceptable threshold level, the translation model of the machine translator may be adjusted to improve subsequent translations performed by the machine translator.

Method and apparatus for evaluating machine translation quality

A machine translation method for translating source text from a first language to target text in a second language includes receiving the source text in the first language and accessing a library of bi-fragments, each of the bi-fragments including a text fragment from the first language and a text fragment from the second language, at least some of the bi-fragments comprising non-contiguous bi-fragments in which at least one of the text fragment from the first language and the text fragment from the second language comprises a non-contiguous fragment.

Machine translation using non-contiguous fragments of text

Parallelization of word alignment for a text-to-text operation. The training data is divided into multiple groups, and training is carried out of each group on separate processors. Different techniques can be carried out to increase the speed of the processing. The hookups can be done only once for all of multiple different iterations. Moreover, parallel operations can apply only to the counts, since this may be the most time-consuming part.

Task parallelization in a text-to-text system

A machine translation (MT) decoder may transform a translation problem into an integer programming problem, such as a Traveling Salesman Problem (TSP). The decoder may invoke an integer program (IP) solver to solve the integer programming problem and output a likely decoding based on the solution.

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Kenji Yamada