Cybernet Systems Corporation

Other

Charles J Cohen

Charles J Jacobus

Glenn J Beach

Douglas Haanpaa

George V Paul

Steve Rowe

Eugene Foulk

Brook Cavell

Jay Obermark

Gene Foulk

Pritpaul Mahal

Kevin Tang

Jean-Pierre Murray

An autonomous vehicle (an AV, or manual vehicle in an autonomous or semi-autonomous mode) includes the ability to sense a command from a source external to the vehicle and modify the behavior of the vehicle in accordance with the command. For example, the vehicle may visualize a police officer or other person associated with traffic control and interpret gestures made by the person causing the vehicle to stop, slow down, pull over, change lanes, back up or take a different route due to unplanned traffic patterns such as accidents, harsh weather, road closings or other situations. The system and method may also be used for non-emergency purposes, including external guidance for load pick-up/placement, hailing a vehicle used as a cab, and so forth. The command may further be spoken or may include a radio frequency (RF) light or other energy component.

Gesture recognition system for autonomous vehicle traffic control

Autonomous and manually operated vehicles are integrated into a cohesive, interactive environment, with communications to each other and to their surroundings, to improve traffic flow while reducing accidents and other incidents. All vehicles send/receive messages to/from each other, and from infrastructure devices, enabling the vehicles to determine their status, traffic conditions and infrastructure. The vehicles store and operate in accordance with a common set of rules based upon the messages received and other inputs from sensors, databases, and so forth, to avoid obstacles and collisions based upon current and, in some cases, future or predicted behavior. Shared vehicle control interfaces enable the AVs to conform to driving activities that are legal, safe, and allowable on roadways. Such activities enable each AV to drive within safety margins, speed limits, on allowed or legal driving lanes and through allowed turns, intersections, mergers, lane changes, stops/starts, and so forth.

Autonomous transportation system and methods

A system and method accepts data from direct fault and parameters measurements and historical data in formatted and unformatted form organized by device or system class, specific unit and unit subsystems, and by operation to learn how to predict or correct specified maintenance or other operations. Disclosed examples use known data and learned information historical data to correct or elaborate service requests based on specified and likely (based on learned information) needed maintenance operations. The system and method are applicable to any device requiring prognostic maintenance or other service based on service orders and direct measurement of operating parameters.

Prognostic maintenance system and method

An in-vehicle system for generating precise, lane-level road map data includes a GPS receiver operative to acquire positional information associated with a track along a road path. An inertial sensor provides time local measurement of acceleration and turn rate along the track, and a camera acquires image data of the road path along the track. A processor is operative to receive the local measurement from the inertial sensor and image data from the camera over time in conjunction with multiple tracks along the road path, and improve the accuracy of the GPS receiver through curve fitting. One or all of the GPS receiver, inertial sensor and camera are disposed in a smartphone. The road map data may be uploaded to a central data repository for post processing when the vehicle passes through a WiFi cloud to generate the precise road map data, which may include data collected from multiple drivers.

System and method for generating precise road lane map data

Automated inventory management and material (or container) handling removes the requirement to operate fully automatically or all-manual using conventional task dedicated vertical storage and retrieval (S&R) machines. Inventory requests Automated vehicles plan their own movements to execute missions over a container yard, warehouse aisles or roadways, sharing this space with manually driven trucks. Automated units drive to planned speed limits, manage their loads (stability control), stop, go, and merge at intersections according human driving rules, use on-board sensors to identify static and dynamic obstacles, and human traffic, and either avoid them or stop until potential collision risk is removed. They identify, localize, and either pick-up loads (pallets, container, etc.) or drop them at the correctly demined locations. Systems without full automation can also implement partially automated operations (for instance load pick-up and drop), and can assure inherently safe manually operated vehicles (i.e., trucks that do not allow collisions).

Automated warehousing using robotic forklifts or other material handling vehicles

Autonomously driven vehicles operate in rain, snow and other adverse weather conditions. An on-board vehicle sensor has a beam with a diameter that is only intermittently blocked by rain, snow, dust or other obscurant particles. This allows an obstacle detection processor is to tell the difference between obstacles, terrain variations and obscurant particles, thereby enabling the vehicle driving control unit to disregard the presence of obscurant particles along the route taken by the vehicle. The sensor may form part of a LADAR or RADAR system or a video camera. The obstacle detection processor may receive time-spaced frames divided into cells or pixels, whereby groups of connected cells or pixels and/or cells or pixels that persist over longer periods of time are interpreted to be obstacles or terrain variations. The system may further including an input for receiving weather-specific configuration parameters to adjust the operation of the obstacle detection processor.

All weather autonomously driven vehicles

A system and method has the ability to take information from a wide variety of sources and package it in a form that a user can accesses in a conversationally intuitive manner. Task or knowledge domain-specific knowledge bases acquired from structured and free-text sources, data extracted describing world state, or natural language and spoken language knowledge are used to 'intelligently' respond to an operator's or user's verbal or written request for information. In the example of a maintenance system, a user may submit status-related questions, and the system might then verbalize a list of instructions of what further diagnostic information the maintainer should acquire through tests. As the maintainer verbalizes to the system their findings, the system might narrow down its assessment of likely faults and eventually verbalize to the maintainer specific steps, and potentially images and diagrams describing the necessary corrective maintenance. Additional applications are presented in the disclosure.

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Ann Arbor, MI, United States of America

Charles J Cohen