Ford Global Technolgoies, LLC

Caterpillar Inc.

Russell Schloss

Basel Alghanem

Benjamin D Ballard

Corey Lee Gorman

Benjamin J Hodel

Mark Ollis

Justin Douglas Speichinger

George Peter Kenneth Carr

Shadi Jammoul

Carsten Thees

Nicholas Payne

Nicolas Vandapel

Christian Höeppner

Yujun Wang

Limin Wu

Systems and methods are described for measuring velocity of an object detected by a light detection and ranging (lidar) system. According to some aspects a method may include receiving a lidar dataset generated by the lidar system, transforming the lidar dataset into a first layer dataset and a second layer dataset, and converting the first layer dataset into a first image and the second layer dataset into a second image. The method may also include performing a feature detection operation that identifies at least one feature in the first image and the same feature in the second image, locating a first location of the feature in the first image and a second location of the feature in the second image, and generating a velocity estimate of the feature based on a difference between the first location and the second location and a difference between the different time intervals.

Systems and method for lidar grid velocity estimation

Systems and methods for complementary control of an autonomous vehicle (AV) are disclosed. The methods include receiving information comprising an active trajectory of an AV that the AV intends to following for a planning horizon. The methods also include using the active trajectory to identify one or more regions in an environment of the AV such as a fallback monitoring region (FMR) and an active monitoring region (AMR), and generating one or more instructions for causing the AV to execute a collision mitigation action in response to an object being detected within the AMR. The methods further include transmitting the one or more instructions to an AV platform (AVP) for execution.

Complementary control system for an autonomous vehicle

A machine control system can store model weights determined via machine learning using a training dataset correlating preset hydraulic valve displacements to measured movement parameters of a machine component. The machine control system can receive an input command for the component and machine state data from machine sensors. A control mapping model can use the model weights to map a combination of the input command and the machine state data into a predicted displacement of the hydraulic valve that causes movement of the component in response to the input command.

Control mapping for hydraulic machines

A sound monitoring system for collecting and processing an environmental noise level surrounding a work machine is disclosed. The sound monitoring system may include an acoustic sensor having a plurality of microphones arranged into a signal detection array to capture the environmental noise level and generate an acoustic output signal. Additionally, the sound monitoring system may include a controller communicably coupled to the acoustic sensor and programmed to perform a signal processing to identify a first portion and a second portion of the acoustic output signal. Furthermore, the controller may be programmed to generate a controller output signal based on the first and second portions of the output level. A speaker may be located on the work machine and communicably coupled to the controller such that the speaker receives and emits the controller output signal.

Sound dampening and pass through filtering

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Pittsburgh, PA, United States of America

Russell Schloss