Member Info for johnchucka

In another embodiment, a robotic teleoperative system for utilizing gaze detection to improve teleoperative robotic object interaction includes a processing device. The robotic teleoperative system also a non-transitory, processor-readable storage medium, the non-transitory, processor-readable storage medium comprising one or more programming instructions that, when executed, cause the processing device to output, to a display of a gaze-tracking device utilized by a user, a representation of an environment of a robot. The instructions further cause the processing device to receive, from the gaze-tracking device, user gaze data corresponding to a gaze area subject to a gaze of the user within the representation. The instructions also cause the processing device to determine a context based upon an object within the gaze area, an object type, and characteristics of the object. The instructions further cause the processing device to receive, via a user interface, a user input. The instructions also cause the processing device to output, to the robot, a contextual command corresponding to the context and the user input.

In yet another embodiment, a system comprises a user worn device having a display that displays an environment of a robot to a user. The system further comprises a gaze tracking module that tracks the gaze of the user in a scene of the environment displayed by the display. The system also comprises a gesture tracking module that tracks the user's other physical movements. The system additionally comprises a processing device that determines a type of object the user is viewing from information received from the gaze tracking module and current actions that the user is completing from information received from the gesture tracking module. The processing device also uses the type of object and current actions to determine a context and the user's intent such that the user's gestures can be accurately mapped to a virtual object movement within the environment of the robot. The processing device additionally instructs the robot to perform the contextual command in a manner that differs from the user's other physical movements.

Embodiments of the present disclosure are directed to methods and systems for determining teleoperating user intent via eye tracking. A user may remotely operate a device, such as a robot, utilizing a gaze-tracking device worn by the user. The gaze tracking device may receive imaging data from the robot to provide a representation of the robot's environment. The gaze tracking device may receive the gaze of the user within the representation of the robot's environment. A context may be determined based upon an object within the gaze area, an object type, and characteristics of the object. After physical movement receiving from the user is received, a contextual command may be output to the robot that corresponding to the context and the object.

Toyota 2020-01-16
METHODS AND SYSTEMS FOR DETERMINING TELEOPERATING USER INTENT VIA EYE TRACKING
Systems and methods for determining teleoperating user intent via eye tracking are provided. A method includes outputting, to a display of a gaze-tracking device utilized by a user, a representation of an environment of a robot. The method further includes receiving, from the gaze-tracking device, user gaze data corresponding to a gaze area subject to a gaze of the user within the representation. The method also includes determining a context based upon an object within the gaze area, an object type, and characteristics of the object. The method further includes receiving, via a user interface, a user input. The method also includes outputting, to the robot, a contextual command corresponding to the context and the user input.

BACKGROUND
Users operate robots to perform a variety of tasks within the robot's environment. However, some objects encountered in the robot's environment may require careful attention. If a user's intended action is misinterpreted or miscommunicated to the robot, the robot may in turn act in a way not intended by the user. This may be due to a variety of reasons, such as inexperience of the user with the particular robot, human error, or an issue with the control interface. Another reason for an unintended action may include network lag due to the roundtrip time for transmitting real world information between a user and a robot.
When a robot is not appropriately operated to complete a task in accordance with the user's intent, objects in the environment may be mishandled and/or damaged. For example, if user intends to have the robot pick up a glass without having properly extended the robot arm to the correct length to grasp a glass, there could be a risk of knocking over the glass, in which the glass could fall, crack, and/or shatter. Therefore, a need exists to detect the intent of a user controlling a robot.
https://worldwide.espacenet.com/publicationDetails/biblio?II=3&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200116&CC=US&NR=2020019237A1&KC=A1#

The driver gaze monitoring system 170 is communicatively coupled to the electronic control unit 102 over the communication path 104. The driver gaze monitoring system 170 may include any device configured to monitor the direction and movement of the driver's gaze. More specifically, the driver gaze monitoring system 170 includes one or more devices to monitor a direction and motion of the driver's eyes relative to his or her head. As such, the driver gaze monitoring system 170 may include one or more eye tracking systems 172 configured to output a direction signal indicative of the direction of gaze of the driver. As an example and not a limitation, the eye tracking system may include one or more cameras or some other optical sensors for detecting light reflected back from the driver's eyes. As a non-limiting example, the light reflected back from the driver's eyes may be near infrared light, which may range from about 700 nanometers to 2500 nanometers in the electromagnetic spectrum. In addition to the eye tracking system 172, the driver gaze monitoring system 170 may also include other systems such as, but not limited to, head tracking systems and facial recognition systems. For example, facial recognition systems may be used to determine the identity of the driver based on an interpupillary distance between a driver's eyes.

The reticle is positioned in a specific location 210 (shown in dashed line). The specific location 210 of the visual indicator 200 is aligned with the direction of gaze of the driver. As explained below, the heads-up display 120 generates the visual indicator 200 upon the windshield 144 in response to determining the driver's gaze is directed towards a location other than a respective location of an object 220. The object 220 is in the obstacle position when a current or future location of the object 220 is determined to intersect with the driving trajectory of the vehicle 100. In the exemplary embodiment shown in FIG. 2, the object 220 is a pedestrian who is about to cross a street 222. However, the object 220 may be any other type of object that the driver of the vehicle 100 may encounter such as, for example, another vehicle, an animal, debris upon the road, and the like.

It should now be understood that embodiments described herein are directed to vehicle systems including a heads-up display that generates one or more indicators to redirect the driver's attention towards an object of interest. More specifically, the current or future location of the object intersects with the driving trajectory of the vehicle. Accordingly, in the event the system determines that driver's gaze is directed towards a location other than the object of interest, then the system redirects the driver's attention towards the object of interest. Thus, the driver will be aware of the object. Once the driver is aware of the object, he or she may maneuver the vehicle in order to avoid contact with the object or any other undesirable circumsta

Toyota 2020-01-16
VEHICLE SYSTEMS AND METHODS FOR REDIRECTING A DRIVER'S GAZE TOWARDS AN OBJECT OF INTEREST
Vehicle systems and methods for redirecting the direction of gaze of a driver towards an object are disclosed. In one embodiment, a vehicle includes an object detection system configured to output an object signal in response to detecting an object, a driver gaze monitoring system configured to output a direction signal, a heads-up display configured to generate a visual indicator, one or more processors and one or more non-transitory memory modules communicatively coupled to the one or more processors and storing machine-readable instructions that, when executed, cause the one or more processors to perform at least at the following: determine an object and a respective location of the object based at least in part on the object signal from the object detection system, and determine a direction of gaze of a driver based at least in part on the direction signal from the driver gaze monitoring system.

The embodiments disclosed herein are directed to vehicle systems and methods to generate a visual indicator that redirects a driver's gaze towards an object of interest using a heads-up display. The object of interest may be an obstacle such as, for example, a pedestrian, another vehicle, debris such as fallen tree limbs, and the like. The system detects the object of interest, and determines if the object is positioned along the vehicle's trajectory. The system also determines a direction of the driver's gaze. The system then compares a respective location of the object with the direction of the driver's gaze. In response to determining the driver's gaze is directed towards a location other than the object, the heads-up display generates the visual indicator. The visual indicator is aligned with the driver's direction of gaze. The visual indicator is configured to attract the driver's attention, and may be any two or three dimensional symbol or object. Some examples of visual indicators include, but are not limited to, a reticle, a cursor, or a polygon.
https://worldwide.espacenet.com/publicationDetails/biblio?II=4&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200116&CC=US&NR=2020018952A1&KC=A1#

My own speculation is that given the industry focus on 5G / V2everything etc and the technology race auto to L4/5 is that whoever can facilitate that Data collection and technological development of calibrated in/out camera fusion most proficiently and most power efficient will win the lions share of the current DMS market as imo that tech advantage will determine the future safety architecture platform.

Paul McGlone
Peak Imminent Engineer.
https://www.linkedin.com/in/paulamcglone/?originalSubdomain=au

Bosch 4y8m
Naveen Kumar Krishnamoorthy
Senior Software Process Engineer - Aspice at Seeing
https://www.linkedin.com/in/naveen-kumar-krishnamoorthy-8654083b/?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAhs2s0BAnRTkoFuZACybKAqEWJHdYA0M2w

Ex Nokia Staff need their own Thread especially as they are numerous and Nokia and Qualcomm have history.
Loren Angela Alatan
DevOps Engineer at Seeing Machines
1
https://www.linkedin.com/in/loren-angela-alatan-0a918151/?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAArhq0EBAGVFTYy-BFUX-8hS2AYxlJiMAxE

TLS,
Would you normally associate the engineering/design of this with a third party rather than inhouse?

Larry Liang
Senior Optical Engineer- Automotive Industry/ Seeing machines
https://www.linkedin.com/in/larry-liang-b1268429/?originalSubdomain=au
Optical development for high performance LED lamp. Optical development, test and certification for automotive lamp. Digital imaging system and machine vision system .

While were talking about Drunk driving the State farm Patents I posted earlier mentions 'inebriated' in it's description (not many do). Not sure who State Farm is working with or partnering tech wise but they look to be very advanced in terms of Telematics and Insurance related patents.
DETAILED DESCRIPTION

'The present embodiments may relate to, inter alia, the reduction of risks associated with impaired driving via detecting an impaired driver and executing a graduated response that brings a vehicle to a safe stop. As it is generally used herein, “impaired” may refer to any condition in which a driver of a smart vehicle is unable to safely operate the smart vehicle. For example, a driver may be impaired if the driver is tired, distracted, inebriated, and/or the like. Additionally, the driver may be impaired due to the onset of a severe medical event, such as a heart attack, seizure or stroke. Accordingly, the present embodiments utilize vehicle sensors to detect the impaired condition and then issue commands via vehicle control systems to reduce the risks associated with driving in an impaired condition'.

Senior Optics Engineer
Role Description
Seeing Machines is engaged on a multitude of programs at the forefront of innovation, being delivered by highly skilled engineers and scientists.
These programs require world-class Optical Design Camera Systems Engineers to support the successful design, development and integration of Seeing Machines DMS technology for customers across the world.
We are seeking the brightest minds to help us continue to create and deliver technologically advanced solutions.
About the opportunity
As part of the Advanced Sensing team you will be the optical designer and engineering lead for internal and external projects – from initial concept design, advising leading global automotive manufacturers on DMS optical design principles, right through to design for testing and manufacture on internal projects.
Our close-knit team fosters an environment of product innovation, rapid product iteration and collaboration covering active and passive image processing in the visible & IR bands.
You will draw on your experience taking a design from concept to manufacturing, incorporating optics and camera systems engineering best practice.
You will be responsible for developing complex electro-optical systems - including prototyping and testing of optical systems consisting of lenses, filters, and image sensors.
Additionally, you will work closely with engineering managers to supporting other system development groups, providing technical expertise on optical system performance – liaising with customers where required.
This is a role for a proven engineering leader, who is highly organised with excellent follow through and follow up skills.

https://seeingmachines.springboard.com.au/jobtools/jncustomsearch.searchResults?in_organid=18900&in_jobDate=All
https://www.seeingmachines.com/careers/

https://seeingmachines.springboard.com.au/jobs/Canberra,%20ACT/SM-2019076

STATE FARM MUTUAL AUTOMOBILE INSURANCE CO 2020-01-07
Systems and methods for graduated response to impaired driving.
Methods and systems for automatically detecting that a driver of a smart vehicle is impaired and subsequently performing a graduated response are provided. According to certain aspects, vehicle sensors may generate and transmit sensor data indicating that the driver of the smart vehicle is impaired. In response, the smart vehicle may analyze the data and determine a primary response to the indication of impairment. The primary response may be performed by the smart vehicle's control systems. Subsequently, the vehicle sensors may generate and transmit another set of sensor data indicating that the driver of the smart vehicle is impaired. In response, the smart vehicle may analyze the data and determine a secondary response to the indication of impairment. The secondary response may be performed by the smart vehicle's control systems.
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=10525979B1&KC=B1&FT=D&ND=3&date=20200107&DB=&locale=en_EP#

Yes BCB , I agree with your 'jumped to another level' comment .

It must have been an interesting/turbulent time at SEE during the Bosch Bid period when DMS was evolving as a core developmental necessity to the new safety platform and with 5G/V2V/V2everything being developed rapidly.
We had SEE Ericsson /Veoneer/ Xilinx /Conti with a plan and potentially GM/ Qualcomm with another. The flies on the wall would have had a ring side seat to that drama.

Sch/ Yes new appointments
Sm2/ I have no idea but the growing headcount of senior established Conti roles moving to senior SEE positions is intriguing.

17
Dirk Braas
Manager - Automotive Projects & Functional Safety at Seeing Machines
https://www.linkedin.com/in/dirkbraas/?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAHKNkIBacGlnnOOI3uC5XS6IsQbbiI96c4

Continental
Total Duration
8 yrs 1 mo

Title
Head of Business Unit CVAM Japan
Dates Employed
Mar 2018 – Aug 2019
Employment Duration
1 yr 6 mos
Location
Yokohama, Kanagawa, Japan
Head of R&D / Engineering Manager
Dates Employed
May 2015 – Feb 2018
Employment Duration
2 yrs 10 mos
Location
Yokohama, Kanagawa, Japan
• Leading the local organization for Commercial Vehicles, Motorcycle, Construction, and Agriculture business (CVAM) in Japan

• Project acquisition for Japanese OEM business

• Global Functional Safety Manager:
- Organizational and technical central contact for CVAM products
- Supporting project managers regarding the interpretation and usage of functional safety-related
documentation and international safety standards

• Profitability analysis on products and organizational performance

• Portfolio development based on profitability and market requirements

• Driving the adaptation of global engineering processes to Japanese customers’ expectations

16
Mario Perez
Hardware Engineering Manager en Seeing Machines
https://www.linkedin.com/in/mario-perez-1515954a/?originalSubdomain=mx

Continental
Total Duration
11 yrs 2 mos

Title
Local Head of Product Development Center Head-up Displays
Dates Employed
Jul 2018 – Dec 2019
Employment Duration
1 yr 6 mos
Location
Guadalajara Area, Mexico

Title
R&D Manager ( Electrical Engineering )
Dates Employed
Mar 2017 – Jul 2018
Employment Duration
1 yr 5 mos
Location
Guadalajara Area, Mexico
Instrument Clusters
Head up Displays
Core Electronics & Display Technologies

Title
Electrical Engineering Supervisor
Dates Employed
Jun 2015 – Mar 2017
Employment Duration
1 yr 10 mos
Location
Greater Detroit Area

Title
Sr EE/HW Engineer - Group Leader
Dates Employed
Nov 2008 – Jun 2015
Employment Duration
6 yrs 8 mos
Group Leader for GM / Nissan / BMW / VW programs
EE / HW development for Instrument Clusters

Honda Res Inst 2020-01-09
ASSISTANCE SYSTEM, METHOD, AND PROGRAM FOR ASSISTING A USER IN FULFILLING A TASK
A system for assisting a user in fulfilling a task, the system comprises a human interface unit for communicating with the user, a task input unit configured to obtain unstructured knowledge source data on the task, and a processor. The processor interprets a user input obtained by the human interface unit. The processor further analyzes the obtained unstructured knowledge source data for generating an internal representation of the task and monitors a task progress in performing the task by interpreting at least one of the user input and image data. The processor generates a support signal based on the generated internal representation and the monitored task progress and outputs the generated support signal, wherein the support signal comprises information on manipulating at least one object or information how to perform the task.

0054] The results from the unit for natural language understanding 3 and the unstructured knowledge source data acquired by the unstructured external information source 2 are provided to a unit for task structuring 4. The unit for task structuring 4 analyzes the input information and generates an internal representation of the task. The internal representation of the task may comprise elements such as the task to be performed, a task context, subtasks, steps, sub-steps, objects involved in the task, parts of these objects, tools, manipulations, as well as communication parameters such as gazes, speech portions, and state variables such as a robot state, a human state, a task environment state, each associated with the structural elements of the task.

[0055] The generated internal representation is a core element of the assistance system 1 and provides a structure for the previously unstructured knowledge source data.

[0056] A human-machine interface unit 5 may include at least one of an acoustic sensor 6 and a visual sensor 7. A simple example for the acoustic sensor 6 is a microphone. An example for the visual sensor 7 is a camera acquiring still images and/or videos from a task environment. The task environment includes a user of the assistance system, objects involved in the task, tools involved in the task, and (autonomous) systems such as a robot cooperating with the human user.
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2020012670A1&KC=A1&FT=D&ND=3&date=20200109&DB=&locale=en_EP#