Member Info for johnchucka

Reposting Terry's find this week with the possibly related Centurion link, which may suggest SEE DMS is likely to be found in Mercedes truck range in the early 2020's.

'On this particular evaluation vehicle Fuso are also testing a driver fatigue monitoring sensor fitted to the dashboard. This may become available as an option, but further down the track'.
http://www.dieselnews.com.au/heavy-duty-trucks-from-fuso/

A while ago I posted this link thinking Centurion would be using Guardian as a after market product. Now I'm wondering if it's a factory fitted DMS that they are eluding too as they also refer to the truck being Semi Autonomous.
https://www.linkedin.com/posts/centurion_2_transport-innovation-autonomousvehicles-ugcPost-6590040273844219904-rqxJ

Semicast Research@semicast_res
Tesla segment up first.
https://www.eetimes.com/document.asp?doc_id=1335230#
https://twitter.com/semicast_res/status/1188057480552685568

A while ago I posted this link thinking Centurion would be using Guardian as a after market product. Now I'm wondering if it's a factory fitted DMS that they are eluding too as they also refer to the truck being Semi Autonomous.
https://www.linkedin.com/posts/centurion_2_transport-innovation-autonomousvehicles-ugcPost-6590040273844219904-rqxJ

Yes Terry that's what it looks like to me ; Not related to todays RNS as far as I can see.
Guardian DMS
Tracto 4 de 200! - Instalando Guardian en compañía de transportes de bebidas.
www.guardiandms.com

#GuardianDMS
translation
Tract 4 of 200! - Installing Guardian in beverage transport company. #GuardianDMS #transportes www.guardiandms.com #bebidas #Cervezas

Head of Sales - Australasia
Company Name
MiX Telematics
Dates Employed
Nov 2018 – Present
Employment Duration
1 yr
Location
Perth, Australia
MiX Telematics (NYSE: MIXT) is a global provider of Fleet Management, Driver Safety, Vehicle and Asset Tracking services and solutions. Responsible for leading direct and channel sales teams in Australasia, I work closely with clients and partners to deliver our Software-as-a-Service (SaaS) solutions in trucks and buses, vans, cars, motorbikes and trailers. With over 760,000+ mobile assets actively managed by MiX Telematics in more than 120 countries, our customers focus on achieving and maintaining the highest levels of safety in a number of industries including but not limited to oil and gas, mining, minerals and resources sectors.#

Seeing Machines
4 yrs
Title
VP Business Development - APAC
Dates Employed
Jul 2018 – Oct 2018
Employment Duration
4 mos
Location
Singapore
With responsibility for leading the sales and business development team for Seeing Machines Guardian computer vision technology in Asia Pacific and Middle East regions. We harnessed human factors science to create and deliver artificial intelligence (AI) technology to our clients that observe driver and operator attention reliably, unobtrusively, and in real time - intervening when necessary.

We have helped some of the most forward thinking Transport and Logistics companies in the world (Toll Group, Air Liquide, Chevron, Shell, Land Transport Authority Singapore, DP World, etc.) improve their safety record through implementation of our life saving technology, drastically reducing the risk of vehicle accidents that occur from fatigue or distracted operators

https://www.linkedin.com/posts/dmsguardian_guardiandms-transportes-bebidas-activity-6592471580146827265-Ktw5

https://www.linkedin.com/in/stevenfletcher1/?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAXKW24BQr14jH7-X1yDK4aZanpVMcrXdFs

Semicast Research@semicast_res
It is a huge leap from software to the successful design, manufacture, distribution & installation of hardware. Guardian almost killed @seeingmachines. If automotive procurement is going so staggeringly well, why bet the company on aftermarket in China?
https://mb.cision.com/Main/15057/2940998/1129466.pdf

INFRARED LIGHT SOURCE PROTECTIVE SYSTEM
Described herein is a monitoring system (100) including one or more infrared light sources (108, 110) for illuminating a subject (102) in a sequenced manner. System (100) also includes a camera (106) for capturing images of the subject (102) during periods in which the subject (102) is illuminated by one of the light sources (108, 110). A processor (118) processes the captured images to determine a brightness measure of the images and a controller (120) controls the output power of the infrared light sources (108, 110) in response to the brightness measure. In response to the processor (118) detecting a brightness measure below a predetermined brightness threshold, the controller (120) is configured to switch off or reduce an output illumination intensity of one of the infrared light sources (108, 110).
https://worldwide.espacenet.com/publicationDetails/biblio?II=0&ND=3&adjacent=true&locale=en_EP&FT=D&date=20191024&CC=WO&NR=2019200434A1&KC=A1#

TRI Contributes to Toyota's New "LQ" Concept Vehicle
TRI CEO Dr. Gill Pratt presents TRI's contributions to Toyota's New "LQ" Concept Vehicle, which includes onboard artificial intelligence agent "Yui" and LQ's automated driving technology.
https://www.youtube.com/watch?v=YSlg2h84ESM&feature=youtu.be

Good find Terry, I have been looking for something like that since Arnika Keenan posted about Fuso. Her other favourites include Scania Australia and Isuzu Australia. .
https://www.linkedin.com/posts/daimler-truck-bus-australia-pacific_daimler-fuso-driversafety-activity-6587957314869960705-19uP
https://www.linkedin.com/posts/isuzu-australia-limited_isuzu-trucks-presenting-new-dual-control-activity-6585040519959846912-m06A

Would a licencing model for all members need to be established for all involved in the work with Autonomous Vehicle Computing Consortium(AVCC)?
https://www.automotiveit.com/autonomous-vehicles/consortium-to-develop-computing-platform-for-autonomous-vehicles/39358.article

Semicast Research@semicast_res
Big moment this: “Dr Gill Pratt, CEO of Toyota Research Institute, says that manufacturers “won’t have a choice” but to accept that without a human driver to blame for an accident, the law is likely to turn to the companies who make the vehicles instead.”
https://twitter.com/semicast_res/status/1186963056934490114

https://twitter.com/ZF_Group/status/1186915296625463296
FUNCTIONAL SAFETY CONCEPT FOR A 3D INTERIOR OBSERVATION CAMERA
https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2019302260A1&KC=A1&FT=D&ND=3&date=20191003&DB=&locale=en_EP#

In this discussion it may be useful to consider what Paul said in the Proactive presentation @ 26mins regarding deals and news etc.
https://www.youtube.com/watch?v=W8GifWJYiKc

If their is collaborative work going on between companies in the AVCC then it's not difficult to imagine how the processes described below could compliment or work with the GM patent . The close timing of these patents and their possible alignment could also give the impression to those looking for patterns of some joint or parallel working between GM, Toyota and SEE.
VEHICLE SYSTEMS AND METHODS FOR DETERMINING A TARGET BASED ON A VIRTUAL EYE POSITION
https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2019228242A1&KC=A1&FT=D&ND=3&date=20190725&DB=&locale=en_EP#
[0028] The user detection system 130 is communicatively coupled to the electronic control unit 102 over the communication path 104. The user detection system 130 may include any device configured to detect the presence of an object within the surrounding environment of the vehicle 100. More specifically, the user detection system 130 is configured to detect the presence of an object within the vicinity of the vehicle 100. The user detection system 130 may include a user detection sensor 132 configured to output an object signal indicative of the presence of one or more objects within the vicinity of the vehicle 100. Based on the object signal of the user detection sensor 132, the electronic control unit 102 may execute object recognition logic to detect an object and classify the detected object into a classification. The user detection sensor 132 may include, but is not limited to, a camera, a LiDAR sensor, a RADAR sensor, a sonar sensor, a proximity sensor, and the like. In some embodiments, the user detection system 130 includes more than one user detection sensor 132.

[0029] As explained below, the electronic control unit 102 calculates the target position 112. The electronic control unit 102 also classifies one or more objects located at the target position 112 based on the information provided by the object signal. More specifically, the electronic control unit 102 executes the object recognition logic to classify the type of object, where the object may be person, another vehicle, a building, and the like.

ONE.
Possibly Related

SYSTEMS AND METHODS OF INCENTIVIZED DATA SHARING
Toyota TRI 2019-10-03

[0067] In arrangements in which the sensor system 120 includes a plurality of sensors, the sensors can function independently from each other. Alternatively, two or more of the sensors can work in combination with each other. In such a case, the two or more sensors can form a sensor network. The sensor system 120 and/or the one or more sensors can be operably connected to the processor(s) 110, the data store(s) 115, and/or another element of the vehicle 100 (including any of the elements shown in FIG. 1). The sensor system 120 can acquire data of at least a portion of the external environment of the vehicle 100 (e.g., nearby vehicles).

[0068] The sensor system 120 can include any suitable type of sensor. Various examples of different types of sensors will be described herein. However, it will be understood that the embodiments are not limited to the particular sensors described. The sensor system 120 can include one or more vehicle sensors 121. The vehicle sensor(s) 121 can detect, determine, and/or sense information about the vehicle 100 itself. In one or more arrangements, the vehicle sensor(s) 121 can be configured to detect, and/or sense position and orientation changes of the vehicle 100, such as, for example, based on inertial acceleration. In one or more arrangements, the vehicle sensor(s) 121 can include one or more accelerometers, one or more gyroscopes, an inertial measurement unit (IMU), a dead-reckoning system, a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system 147, and/or other suitable sensors. The vehicle sensor(s) 121 can be configured to detect, and/or sense one or more characteristics of the vehicle 100. In one or more arrangements, the vehicle sensor(s) 121 can include a speedometer to determine a current speed of the vehicle 100.

[0069] Alternatively, or in addition, the sensor system 120 can include one or more environment sensors 122 configured to acquire, and/or sense driving environment data. “Driving environment data” includes and data or information about the external environment in which an autonomous vehicle is located or one or more portions thereof. For example, the one or more environment sensors 122 can be configured to detect, quantify and/or sense obstacles in at least a portion of the external environment of the vehicle 100 and/or information/data about such obstacles. Such obstacles may be stationary objects and/or dynamic objects. The one or more environment sensors 122 can be configured to detect, measure, quantify and/or sense other things in the external environment of the vehicle 100, such as, for example, lane markers, signs, traffic lights, traffic signs, lane lines, crosswalks, curbs proximate the vehicle 100, off-road objects, etc.

TWO.

[0070] Various examples of sensors of the sensor system 120 will be described herein. The example sensors may be part of the one or more environment sensors 122 and/or the one or more vehicle sensors 121. Moreover, the sensor system 120 can include operator sensors that function to track or otherwise monitor aspects related to the driver/operator of the vehicle 100. However, it will be understood that the embodiments are not limited to the particular sensors described.

[0071] As an example, in one or more arrangements, the sensor system 120 can include one or more radar sensors 123, one or more LIDAR sensors 124, one or more sonar sensors 125, and/or one or more cameras 126. In one or more arrangements, the one or more cameras 126 can be high dynamic range (HDR) cameras, infrared (IR) cameras and so on. In one embodiment, the cameras 126 include one or more cameras disposed within a passenger compartment of the vehicle for performing eye-tracking on the operator/driver in order to determine a gaze of the operator/driver, an eye track of the operator/driver, and so on.

[0072] The vehicle 100 can include an input system 130. An “input system” includes any device, component, system, element or arrangement or groups thereof that enable information/data to be entered into a machine. The input system 130 can receive an input from a vehicle passenger (e.g. a driver or a passenger). The vehicle 100 can include an output system 135. An “output system” includes any device, component, or arrangement or groups thereof that enable information/data to be presented to a vehicle passenger (e.g., a person, a vehicle passenger, etc.).
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2019306241A1&KC=A1&FT=D&ND=3&date=20191003&DB=&locale=en_EP#

One.
This is the most recent SEE patent * and looks to me to be closely related to the GM Patent of 2019-10-17. The patent talks of driver gaze camera and forward facing camera alignment. Scrolling down to 81/82/83 the commentary talks of networks of processors which maybe related to the GM statement ' In such embodiments, the data from the vehicles is generally indicates the gaze of other drivers when at the same location as the vehicle'.

*SYSTEM AND METHOD FOR IDENTIFYING A CAMERA POSE OF A FORWARD FACING CAMERA IN A VEHICLE
A method includes capturing images of a vehicle driver's face from a driver facing camera and images of a forward road scene from a forward facing camera. The images are analyzed to derive gaze direction data in a vehicle frame of reference. The gaze direction data are statistically collated to determine a frequency distribution of gaze angles. One or more peaks in the frequency distribution are identified and associated with corresponding reference points in the images to determine one or more reference gaze positions in the vehicle frame of reference. The one or more reference gaze positions are correlated with a position of the reference points in a forward facing camera frame of reference to to determine a camera pose of a forward facing camera in the vehicle frame of reference.
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2019266751A1&KC=A1&FT=D&ND=3&date=20190829&DB=&locale=en_EP#
GM Patent; https://patents.justia.com/patent/20190318180

SYSTEM AND METHOD FOR IDENTIFYING A CAMERA POSE OF A FORWARD FACING CAMERA IN A VEHICLE

[0081] In alternative embodiments, the one or more processors operate as a standalone device or may be connected, e.g., networked to other processor(s), in a networked deployment, the one or more processors may operate in the capacity of a server or a user machine in server-user network environment, or as a peer machine in a peer-to-peer or distributed network environment. The one or more processors may form a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

[0082] Note that while diagrams only show a single processor and a single memory that carries the computer-readable code, those in the art will understand that many of the components described above are included, but not explicitly shown or described for clarity. For example, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0083] Thus, one embodiment of each of the methods described herein is in the form of a computer-readable carrier medium carrying a set of instructions, e.g., a computer program that is for execution on one or more processors, e.g., one or more processors that are part of web server arrangement. Thus, as will be appreciated by those skilled in the art, embodiments of the present disclosure may be embodied as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, or a computer-readable carrier medium, e.g., a computer program product. The computer-readable carrier medium carries computer readable code including a set of instructions that when executed on one or more processors cause the processor or processors to implement a method. Accordingly, embodiments of the present disclosure may take the form of a method, an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware embodiments. Furthermore, the present disclosure may take the form of carrier medium (e.g., a computer program product on a computer-readable storage medium) carrying computer-readable program code embodied in the medium.
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2019266751A1&KC=A1&FT=D&ND=3&date=20190829&DB=&locale=en_EP#