Xerox Corporation

Conduent Business Services, LLC

Gm Global Technolgoy Operations, Inc.

Other

University of Rochester

Orhan Bulan

Robert P Loce

Edgar A Bernal

Aaron Michael Burry

Yao Rong Wang

Wencheng Wu

Peter Paul

David Hahn Clifford

Vladimir Kozitsky

Beilei Xu

Gaurav Sharma

Yusuf Oguzhan Artan

Yehenew G Mengistu

Zhigang Fan

Vishal Monga

Graham Stephen Pennington

Sheetal Mahesh

Safwan R Wshah

Stuart Alan Schweid

Howard A Mizes

David Preston Cummins

Jess Robert Gentner

Michael A Losh

Mason D Gemar

David J Hiemenga

Edul N Dalal

Palghat Srinivas Ramesh

Christopher L Oesterling

Matthew Adam Shreve

Mohannad F Murad

Thomas F Wade

Hengzhou Ding

Abu Saeed Islam

Jayant Kumar

Claude Fillion

Norman W Zeck

Paul H Pebbles

Orhan Demirovic

Keun Jae Kim

Daniel Taylor

Robert R Miles

Hrushit B Raval

Andrew J Lingg

Henryk K Blasinski

Panya Chanawangsa

Eric Hatfield

David Martin Todd Jackson

Mohammed H Al Qizwini

Benjamin J Cool

Mathew Darst

Dawn M Chorbagian

Xuewei Qi

In one example implementation according to aspects of the present disclosure, a computer-implemented method for generating map features includes receiving, by a processing device, aerial image data. The method further includes receiving, by the processing device, telemetry data. The method further includes performing, by the processing device, data fusion on the aerial image data and the telemetry data to generate map features. The method further includes controlling, by the processing system, a vehicle based at least in part on the map features.

Generating map features based on aerial data and telemetry data

A system comprises a processor and a memory storing instructions. The processor receives an image for processing using a reinforcement learning based agent comprising a neural network trained using a reward function. The image includes N lane lines of a roadway, where N is a positive integer. The instructions configure the processor to traverse the image using the agent at least N times from a first end of the image to a second end of the image by: incrementally moving the agent from a first side of the image to a second side of the image after each traversal; and maximizing rewards for the agent using the reward function during each traversal of the image using the agent. The instructions configure the processor to identify the N lane lines of the roadway as a single cluster of lane lines after traversing the image using the agent at least N times.

Identification and clustering of lane lines on roadways using reinforcement learning

System and methods for estimating a centerline of a road that separates traffic moving in opposite directions include aggregating a data set from each of a plurality of vehicles traversing the road over a period of time as telemetry data. Each data set of the telemetry data indicates a location and a heading. The method includes clustering the data sets of the telemetry data based on the heading indicated by each data set, and identifying a separator to separate the data sets indicating a first heading from the data sets indicating a second heading, opposite to the first heading. The centerline is estimated based on applying a spatial smoothing to the separator.

Estimation of road centerline based on vehicle telemetry

A system for inferring lane boundaries via vehicle telemetry for a road section is provided. The system includes a computerized remote server device operable to receive through a communications network sensor data describing lane markings upon roads bordering the road section, determine established lanes upon roads bordering the road section based upon the sensor data, receive through the communications network the vehicle telemetry generated by a plurality of vehicles traversing the road section, generate inferred lanes for the road section based upon the vehicle telemetry, match the inferred lanes to the established lanes to generate unified lane geometries, and publish the unified lane geometries.

Inferring lane boundaries via high speed vehicle telemetry

A method to detect a roadway edge includes calculating a first likelihood of a roadway edge from an aerial image of a roadway by shifting a centerline of the roadway perpendicular to the centerline and overlapping the centerline with image gradients. A second likelihood of the roadway edge is determined using a vehicle telemetry fitting a probability distribution to telemetry points along the roadway. The first likelihood of the roadway edge and the second likelihood of the roadway edge are fused to identify a final likelihood of the roadway edge.

Detecting road edges by fusing aerial image and telemetry evidences

Systems and methods are provided for generating a map for use in controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, aerial image data depicting an environment; processing, by the processor, the aerial image data with a plurality of trained deep learning models to produce a predicted radar map; and controlling the vehicle based on the predicted radar map.

Methods and systems for generating radar maps

According to exemplary methods, automated image processing of images from order locations of a multi-lane drive-thru system is continually performed using an automated ordering system. It is automatically determined if a vehicle is present at any of the order locations based on the automated image processing. It is automatically determined if an order has been initiated into the automated ordering system. When the order has been initiated while the vehicle is present at any of the order locations, an automated sequencing processing is automatically begun. The automated sequencing processing determines an order pick-up sequence for picking up orders at pickup windows of the multi-lane drive-thru system. When the order has been initiated while the vehicle is not present at any of the order locations, a potential out-of-sequence warning is automatically associated with the order and output from the automated ordering system.

System and method for lane merge sequencing in drive-thru restaurant applications

According to exemplary methods of training a convolutional neural network, input images are received into a computerized device having an image processor. The image processor evaluates the input images using first convolutional layers. The number of first convolutional layers is based on a first size for the input images. Each layer of the first convolutional layers receives layer input signals comprising features of the input images and generates layer output signals that include signals from the input images and ones of the layer output signals from previous layers within the first convolutional layers. Responsive to an input image being a second size larger than the first size, additional convolutional layers are added to the convolutional neural network. The number of additional convolutional layers is based on the second size in relation to the first size. The additional convolutional layers are initialized using weights from the first convolutional layers. Feature maps comprising the layer output signals are created.

System and method for expanding and training convolutional neural networks for large size input images

A smoke detection system for an interior of a vehicle is provided. The smoke detection system includes a detection module and a reporting module. The detection module, while the vehicle is being used as part of a car sharing or ride sharing service: receives a sensor signal from a smoke detector; compares data included in the sensor signal to smoke characteristic or smoke pattern data; based on the comparison, determines whether an occupant of the vehicle is smoking; and generates an alert signal if the occupant is smoking. The reporting module at least one of: based on the alert signal, visually or audibly provides a warning to the occupant to stop smoking; transmits the alert signal to a mobile device of the occupant to indicate that a smoking event has been detected; or transmits the alert signal to a network device of a service provider to report the smoking event.

In-vehicle smoke detection and reporting system and method for car sharing and ride sharing vehicles

A system for partitioning a two-dimensional (2D) array and recognizing elements of the 2D array selects a location in an array comprising pixel data of the 2D array; and selects a direction in which to traverse the array from the selected location to segment the elements of the 2D array. The system uses a model and a reward/penalty function to guide the traversal through the array. The system changes direction when the data encountered while traversing the array in the selected direction represents one of the elements of the 2D array and continues in the selected direction when the data represents a joint between two elements of the 2D array. The system recognizes one or more of the elements of the 2D array segmented by traversing the array.

Active segmention of scanned images based on deep reinforcement learning for OCR applications

Systems and methods of mapping traffic related objects. In one embodiment, a method includes: receiving image sensor data relating to an environment associated with a vehicle; receiving lidar sensor data relating to the environment associated with the vehicle; selecting, by a processor, at least one image region including a traffic related object from the image sensor data; selectively filtering, by a processor, the at least one image region based on the lidar sensor data; and generating, by a processor, a composite map including a representation of the traffic related object superimposed upon a defined map of the environment based on the selectively filtering.

Systems and methods for detection, classification, and geolocation of traffic objects

A method and apparatus for detecting and classifying objects associated with a vehicle are provided. The method includes capturing a first image and a second image of a cabin of a vehicle, dividing the first image and the second image into a plurality of regions, comparing a first region of the first image to a second region of the second image, in response to detecting a difference between the second region and the first region, classifying an object present in the difference between the second region and the first region and labeling the classified object, and providing a notification of the classified object to at least one from among an occupant of the vehicle and an operator of the vehicle.

Method and apparatus for detecting and classifying objects associated with vehicle

This disclosure provides a method and system for automated sequencing of vehicles in side-by-side drive-thru configurations via appearance-based classification. According to an exemplary embodiment, an automated sequencing method includes computer-implemented method of automated sequencing of vehicles in a side-by-side drive-thru, the method comprising: a) an image capturing device capturing video of a merge-point area associated with multiple lanes of traffic merging; b) detecting in the video a vehicle as it traverses the merge-point area; c) classifying the detected vehicle associated with traversing the merge-point area as coming from one of the merging lanes; and d) aggregating vehicle classifications performed in step c) to generate a merge sequence of detected vehicles.

Method and system for automated sequencing of vehicles in side-by-side drive-thru configurations via appearance-based classification

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Henrietta, NY, United States of America

Orhan Bulan