Member Info for johnchucka

Due to the COVID-19 affecting transport and import business, VTA welcomes the new changes of the increase asset write-off from $30,000 to $150,000 and to include business with turn over less than $500 Million..

Wither your business is looking to invest in compliance technology, Asset tracking or for the Guardian by Seeing Machines, contact me today to secure your chance before end of June 2020.

Investing in the Guardian by Seeing Machines - A real time Fatigue and Distraction Accident Prevention Technology, your business can ensure the safety of your drivers, and the protection of your brand.
https://www.smh.com.au/politics/federal/extraordinary-increase-in-business-asset-write-off-scheme-under-stimulus-plan-20200312-p549cw.html

https://www.linkedin.com/posts/activity-6645125905985474560-e6f9

Can they afford not to?

Monumental bailouts I would imagine.

Method and control arrangement for transition between modes of a vehicle
P7 Original documents
20
The control arrangement 120 may determine to switch into autonomous driving mode eg when the driver is detected to fall asleep , to be non focused on environmental traffic situation , to be affected by alcohol or drugs etc which may be detected by one or several sensors directed towards the driver such as eg a driver monitoring camera, video camera , a heart rate monitor, a blood pressure monitor, a respiration meter etc and/or a vehicle displacement detector.
https://worldwide.espacenet.com/publicationDetails/originalDocument?CC=SE&NR=1950530A1&KC=A1&FT=D&ND=3&date=20200203&DB=&locale=en_EP#

Qualcomm / FCA / Harman etal 5G Demos.
https://5gaa.org/5gaa-in-motion/news/

SYSTEM AND METHOD FOR CONTROLLING MACHINES USING OPERATOR ALERTNESS METRICS
A method includes determining a first task requiring one of a plurality of machines to traverse along a first route and determining a second task requiring the one of the plurality of machines to traverse along a second route. The method also includes determining a first route score for the first route, the first route score being at least in part indicative of a difficulty of traversing along the first route, and determining a second route score for the second route, the second route score being at least in part indicative of a difficulty of traversing along the second route. The method may also include receiving sensor data generated by a sensor associated with a first machine of the plurality of machines and determining, based at least in part on the sensor data, an alertness metric associated with an operator of the first machine. Based at least in part on the alertness metric being equal to or greater than a threshold alertness metric, the method may also include determining the first task for completion by the first machine or determining the second task for completion by the first machine. The method may also include sending, to an electronic device associated with the operator of the first machine, information about the first route or the second route; and receiving confirmation information indicative of the first machine traversing along the first route or of the first machine traversing along the second route.

More specifically, FIG. 1 schematically illustrates that the computing system 128 may include an alertness determination component 130. In examples described herein, the alertness determination component 130 may analyze sensor data and/or additional information to characterize an alertness of a machine operator. For example, and as described in more detail with regard to FIGS. 2 and 3, the alertness determination component 130 may determine an alertness metric for an operator based on sensor data, e.g., camera data, taken of the operator while the operator is in the cabin of the vehicle. In other examples, the sensor data may be received from a device associated with the operator, which may be a wearable device in some instances. Additional data that may be considered can include information about the operator, e.g., work history, experience level, route history, or the like, and/or information about the machine(s), e.g., wear information, maintenance history, or the like.
https://worldwide.espacenet.com/publicationDetails/biblio?II=2&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200312&CC=US&NR=2020080851A1&KC=A1#
https://patents.justia.com/patent/20200080851
Drawings
http://www.freepatentsonline.com/20200080851.pdf

A camera calibration device includes: a storage unit configured to store an allowable range of a camera position posture including at least one of a position and an angle of an in-vehicle camera in association with a reference riding pattern that is a reference of an occupant's riding pattern with respect to a vehicle; an acquisition unit configured to acquire the riding pattern and the camera position posture at a preset timing; and a control unit configured to execute at least one of notification of optical axis deviation of the in-vehicle camera and correction of the optical axis deviation when the acquired camera position posture is not within the allowable range associated with the reference riding pattern that matches the acquired riding pattern in the storage unit.
https://patents.justia.com/patent/20200082568
https://worldwide.espacenet.com/publicationDetails/biblio?II=4&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200312&CC=US&NR=2020082568A1&KC=A1#
Drawings
http://www.freepatentsonline.com/20200082568.pdf

METHOD, DEVICE AND COMPUTER-READABLE STORAGE MEDIUM WITH INSTRUCTIONS FOR SETTING A HEAD-UP DISPLAY IN A TRANSPORTATION VEHICLE
A method, a device and a computer-readable storage medium containing instructions for setting a head-up display in a transportation vehicle. A setting device is introduced into a defined position in the transportation vehicle. The setting device has at least one camera and one projection unit. The projection unit is used to project an alignment pattern onto a calibration target positioned outside the transportation vehicle. The calibration target and the alignment pattern are acquired using a calibrated camera installed in the transportation vehicle. A deviation of the setting device from the defined position in the transportation vehicle is determined based on the image data of the calibrated camera. At least one test image displayed by the head-up display is acquired using the camera of the setting device. Setting information for the head-up display is determined based on the at least one acquired test image.

[0009]
DE 10 2015 008 887 A1 describes a method for calibrating a head-up display in a vehicle, in which a virtual image output by the head-up display is compared with a possible eye position of a driver. In the course of a calibration process, a measuring unit provided with a marking is positioned at a possible head position of the driver and a measuring target located in front of the vehicle is detected. An image recording device for determining the possible eye position of the driver records the marking of the measuring unit at the same time. An electronic correction for calibrating the virtual image of the head-up display is determined from a determined offset of the measuring unit and the image recording device.

[0010]
The calibration is currently usually carried out on a test bench for driver assistance systems at the end of the production line. On the test bench, after the vehicle has been aligned in the test bench, a camera is brought to the most likely eye position of the driver by means of a mechanically movable telescopic arm, i.e. the middle of the so-called eyebox. This position is specified based on a simulated ideal vehicle. Any deviations of the real vehicle with regard to the position in the test bench and in the room are determined by laser sensors by means of a detection of the wheel house edges and taken into account when positioning the camera. Various test images are then displayed, which are used to determine the position of the displayed image as well as the distortion and image errors in the display. This information is then written into the corresponding control unit, which then takes these deviations into account when displaying the driving information.
https://worldwide.espacenet.com/publicationDetails/biblio?II=2&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200312&CC=US&NR=2020082570A1&KC=A1#

Visual line movement-related value acquisition device, transport apparatus equipped with same, and visual line movement-related value acquisition method

[0104] An area of an effective visual field of a person changes in accordance with an internal state of the person. As used herein, the term "internal state" means an attention level (a state), an awake level (a state), a concentration level (a degree of concentration), a margin, information processing performance, or the like. Furthermore, the internal state is not limited to a mental state and also means a physical state (a fatigue level).

[0105] Specifically, the effective visual field of the person tends to be narrower as the internal state of the person becomes poorer. In FIG. 2, each of the reference signs A and B schematically denotes an effective visual field of a same person in accordance with internal states of the person. The effective visual field B is a range of a field of vision when the 22 Translation of PCT/JP2018/030055 _LJUUV ffL%) ,L. _L V(I U UVV'JffA internal state is poorer than that of the effective visual field A. As illustrated in FIG. 2, the effective visual field B when the internal state is poorer, is narrower than the effective visual field A.

[0106] Therefore, there is a probability that the effective visual field of the person is detected, and thereby, the internal state of the person can be understood. This embodiment focuses on the visual line movement of the person in order to detect the effective visual field of the person.

[0107] People detect a visual object to be gazed at next by using information achieved from inside of the effective visual field and perform visual line movement by eye motion and head motion on the detected visual object. When the visual object is located inside the effective visual field, the eye motion precedes the head motion. On the other hand, when the visual object is located outside the effective visual field, the head motion precedes the eye motion. As described above, depending on whether or not the visual object is located inside the effective visual field, an action of viewing the visual object by people (which will be hereinafter referred to as a viewing action) differs.

https://worldwide.espacenet.com/publicationDetails/description?CC=AU&NR=2018316010A1&KC=A1&FT=D&ND=3&date=20200305&DB=&locale=en_EP#

LOW POWER MODE FOR ONE OR MORE CAMERAS OF A MULTIPLE CAMERA SYSTEM
Aspects of the present disclosure relate to systems and methods for placing a camera into a low power mode. An example device may include a memory and a processor. The processor is configured to determine that the first camera is to be placed into a low power mode. The first camera is in an active mode and has a first frame capture rate. The processor further is configured to generate and output a command for placing the first camera into the low power mode. Execution of the command causes the first camera to increase the first frame capture rate to a second frame capture rate and go into the low power mode from the active mode after increasing the first frame capture rate to the second frame capture rate.
https://patents.justia.com/patent/20200084387

Semicast Research@semicast_res
Really well written paper but it looks out of date already compared with current capabilities of direct DMS to detect driver state.
“Addressing driver disengagement and system misuse: human factors recommendations for Level 2 driving automation design”
https://www.iihs.org/topics/bibliography/ref/2200

Semicast Research@semicast_res
Great work IIHS & aligns with recs from NTSB for need for IR vision DMS in partially automated vehicles. Again highlights critical role of human factors science in development of driver monitoring. Auto DMS is much more than IR sensor & imaging SW. Barriers to entry are immense.

IIHS has issued a set of research-based safety recommendations on the design of partially automated driving systems. The guidelines emphasize how to keep drivers focused on the road even as the vehicle does more of the work. https://go.iihs.org/news-automation-safeguards…
https://www.iihs.org/news/detail/automated-systems-need-stronger-safeguards-to-keep-drivers-focused-on-the-road

I Live in Italy’s “Orange Zone”: What it’s Like Inside the Lockdown

However, many companies have restrictions and precautions of some kind in place now: for example, GLS delivery drivers must wear hand masks and gloves “at all times” while making their rounds. Travelling for work is for all purposes unrestricted except inside the “red zone” (the original area of the contagion). But visiting foreign customers right now may be a tad complicated. I’ll keep you updated as soon as I find out more.

https://wolfstreet.com/2020/03/11/i-live-in-italys-orange-zone-what-its-like-inside-the-lockdown/

Also @52sec lights on the left and right side of the cab which looks similar to 104/6 in Fig 1 of the SEE patent SYSTEM AND METHOD OF CAPTURING TRUE GAZE POSITION DATA
https://worldwide.espacenet.com/publicationDetails/mosaics?CC=WO&NR=2018170538A1&KC=A1&FT=D&ND=3&date=20180927&DB=&locale=en_EP#

Since I assume Diamler are not doing a Tesla I'm wondering what is that small rectangular box sitting on the right hand display @46sec ?
Daimler Cascadia Level 4 Autonomous Driving - 2020
https://www.youtube.com/watch?v=cyVHJNvjRMo

Always thought that Xilinx would join AVCC but don't think I've seen it mentioned before or publicised. I May have just forgot.
https://www.avcconsortium.org/aboutus

All regulars here have a dog in the race imo.

One wonders what DMS capacity may be found in the new Volvo FMX, FH, FH16 and FM considering the longstanding SEE partnership.

https://www.youtube.com/watch?time_continue=201&v=FDGUDLQQBhM&feature=emb_logo

Posted for reference.

Training of Vehicles to Improve Autonomous Capabilities
Systems and methods of training vehicles to improve performance, reliability and autonomy of vehicles. Sensors capture a vehicle driver's eye movements, hand grip and contact area on steering wheel, distance of the accelerator and brake pedals from the foot, depression of these pedals by the foot, and ambient sounds. Data from these sensors is used as training data to improve autonomy of vehicles. Detection of events occurring outside a vehicle is made by comparing human sensor data with an associated road map to determine correlation. Signatures corresponding to human reactions and actions are extracted, which also incorporate data acquired using vehicle and outside environment sensors. By scoring driver responses to events outside the vehicle as well as during non-events, expert drivers are identified, whose responses are used as a part of vehicle training data.
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2020074200A1&KC=A1&FT=D&ND=3&date=20200305&DB=&locale=en_EP#

https://www.linkedin.com/in/microbubble/?originalSubdomain=au
https://verless.com/