Fca US LLC

Gm Global Technolgoy Operations, Inc.

Rohit S Paranjpe

Dalong Li

Fanghui Shi

Xinyu Zhou

Xu Han

Stephen W Horton

Uday P Korde

Pochuan B Hsing

Donald R Jones

Benjamin J Chappell

Annette Cusenza

Joseph P Kelly

Julian J Blair

Chandrashekar Joshi

Wensheng Zhang

Yi-Hsin Chen

Kanwerdip Singh

Vehicle perception techniques include applying a 3D DNN to a set of inputs to generate 3D detection results including a set of 3D objects, transforming the set of 3D objects onto a set of images as a first set of 2D bounding boxes, applying a 2D DNN to the set of images to generate 2D detection results including a second set of 2D bounding boxes, calculating mean average precision (mAP) values based on a comparison between the first and second sets of 2D bounding boxes, identifying a set or corner cases based on the calculated mAP values, and re-training or updating the 3D DNN using the identified set of corner cases, wherein a performance of the 3D DNN is thereby increased without the use of expensive additional manually and/or automatically annotated training datasets.

Automatic evaluation of three-dimensional vehicle perception using two-dimensional deep neural networks

Vehicle perception techniques include obtaining a training dataset represented by N training histograms, in an image feature space, corresponding to N training images, K-means clustering the N training histograms to determine K clusters with respective K respective cluster centers, wherein K and N are integers greater than or equal to one and K is less than or equal to N, comparing the N training histograms to their respective K cluster centers to determine maximum in-class distances for each of K clusters, applying a deep neural network (DNN) to input images of the set of inputs to output detected/classified objects with respective confidence scores, obtaining adjusted confidence scores by adjusting the confidence scores output by the DNN based on distance ratios of (i) minimal distances of input histograms representing the input images to the K cluster centers and (ii) the respective maximum in-class.

Vehicle perception by adjusting deep neural network confidence valves based on k-means clustering

A bolted joint for coupling two components that undergo thermal expansion and contraction includes a bolt having a head and a stem, the bolt configured to provide a clamping force between the two components. A first washer having a thermal resistant, low friction coating is configured to be disposed between one of the two components and the bolt head to enable the one component to slide relative to the bolt during the thermal expansion and contraction, while maintaining the clamping force, to facilitate preventing bending or shearing of the bolt.

High temperature resistant low friction washer and assembly

An engine control system comprises a coolant temperature weighting module that generates a weighting signal based on coolant temperature. A composite temperature generating module generates a composite temperature based on the coolant temperature, an oil temperature and the weighting signal. A delta friction torque module calculates delta friction torque of an engine based on the composite temperature. An engine operating parameter module that adjusts an engine operating parameter based on the delta friction torque.

Systems and methods for predicting engine delta friction torque using both coolant and oil temperature

A method for calibrating a physical test cell includes the steps of: determining a set of inputs to be provided to the physical test cell based in part on a set of historical test data; providing the inputs to the physical test cell and receiving a set of outputs associated therewith, wherein the providing includes implementing a sequential space filling sampling procedure to substantially cover a region defined by the set of historical values; creating a virtual test cell comprising one or more response surfaces based on the set of outputs; and interrogating the virtual test cell to determine a calibration relationship between at least one of the inputs and at least one of the outputs. Smooth Kriging may be used to determine the virtual test cell.

Methods and apparatus for a virtual test cell

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Rochester Hills, MI, United States of America

Rohit S Paranjpe