Member Info for johnchucka

Adding Denso to Toyota and Panasonic emerging list of similarly described Patents around the subject of Driver Arousal.

PANASONIC IP MAN CO LTD 2020-02-20
SYSTEM AND METHOD FOR ASSESSING AROUSAL LEVEL OF DRIVER OF VEHICLE THAT CAN SELECT MANUAL DRIVING MODE OR AUTOMATED DRIVING MODE
https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2020054264A1&KC=A1&FT=D&ND=3&date=20200220&DB=&locale=en_EP#

TOYOTA MOTOR CO LTD 2020-02-13
DRIVING CONSCIOUSNESS ESTIMATION DEVICE
https://worldwide.espacenet.com/publicationDetails/biblio?II=0&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200213&CC=US&NR=2020047765A1&KC=A1#

DENSO 2020-01-09
AROUSAL LEVEL DETERMINATION DEVICE
An arousal level determination device, which determines an arousal level of a driver driving a vehicle, includes a detection part and a determination part. The detection part is configured to detect an amount of body movement indicating a magnitude of movement of a body of the driver. The determination part is configured to determine the arousal level of the driver. The determination part determines that the driver is in an initial stage of decline in the arousal level when the amount of body movement has a decreasing trend.
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2020008732A1&KC=A1&FT=D&ND=3&date=20200109&DB=&locale=en_EP#

Ace Training Centre .
The unique eye tracking technology used in the Toll AW-139 Full Flight Simulator at The ACE Training Centre, supports aviation training instructors, like Darryl Humphreys, AW139 Standards Manager and Flight Examiner. “For instructors, the ability to see in real time where a student’s eyes are tracking is remarkable. You can observe, analyse and interpret the pilot’s situational awareness.”
The technology provides informed evidence-based data into pilot scanning techniques and situational awareness.
https://www.linkedin.com/company/ace-training-centre/

up this thread I have posted some useful Coronavirus detail non SEE related here
https://www.lse.co.uk/ShareChat.asp?ShareTicker=BMD&share=Baronsmead-2Vt

https://www.peakprosperity.com/coronavirus-infections-outside-of-china-are-growing-exponentially/?utm_source=dlvr.it&utm_medium=twitter

Recommended by Chris Martinson
https://www.amazon.co.uk/Pukka-Herbs-Elderberry-Syrup-100ml/dp/B01N9GT3RV/ref=pd_sbs_121_t_0/259-9252322-8397650?_encoding=UTF8&pd_rd_i=B01N9GT3RV&pd_rd_r=a7aa4e48-5102-4de8-a23c-3a768b0ac76c&pd_rd_w=9JgtD&pd_rd_wg=BIuT5&pf_rd_p=e44592b5-e56d-44c2-a4f9-dbdc09b29395&pf_rd_r=9BMND0CV4Y614H75V0RS&psc=1&refRID=9BMND0CV4Y614H75V0RS

I agree but the Volumes are very low so 1% accumulation would take longer. It went down 1.78% on 7 sales and then down to -3.55% on 17 sales. Looks incredible small volumes and to my mind the bottom of the barrel.

Baxter
Would you agree that the pattern of selling over recent months is closely correlated to the pattern of Lombard buying and subsequent issue of Holdings RNS's?

TLS
A skim read of these closely dated and similarly described Patents (Below) makes me wonder if this is an example of what Gill Pratt meant when he said Guardian for ALL ?

PANASONIC IP MAN CO LTD 2020-02-20
SYSTEM AND METHOD FOR ASSESSING AROUSAL LEVEL OF DRIVER OF VEHICLE THAT CAN SELECT MANUAL DRIVING MODE OR AUTOMATED DRIVING MODE
[0064] As illustrated in Fig. 1 , the arousal level estimation device 10 includes the vehicle behavior detection unit 11, the first arousal level recognition unit 12, the arousal level estimation unit 15, the physiological information detection unit 16, and the second arousal level recognition unit 17. Note that the arousal level (sleepiness) of the driver that is estimated by the arousal level estimation device 10 is decided as any one of five levels of level 1 to level 5, for example. For example, level 1 indicates "not sleepy state", and level 2 indicates "slightly sleepy state". Level 3 indicates "sleepy state", and level 4 indicates "fairly sleepy state". Level 5 indicates "very sleepy state".
https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2020054264A1&KC=A1&FT=D&ND=3&date=20200220&DB=&locale=en_EP#

TOYOTA MOTOR CO LTD 2020-02-13
DRIVING CONSCIOUSNESS ESTIMATION DEVICE
[0057] The driving readiness degree is a level of readiness for the driving by the driver. The driving readiness degree is also called “readiness”. As the driving consciousness and the degree of readiness of the driver against the traveling environment of the host vehicle increase, the value of driving readiness degree increases. As an example, the driving readiness degree here can be divided into five stages such as Low (low), Medium Low (slightly low), Medium (normal), Medium High (slightly high) and High (high). The way of expressing the driving readiness degree is not particularly limited, and may be expressed by a numerical value or may be divided into plural stages.
https://worldwide.espacenet.com/publicationDetails/biblio?II=0&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200213&CC=US&NR=2020047765A1&KC=A1#

[0096] In this embodiment, the physiological feature extraction unit 171 includes one or more feature extraction units and extracts at least one or more features that indicate the physiological state of the driver. For example, as illustrated in Fig. 4 , the physiological feature extraction unit 171 has a face image extraction unit 181, an eye opening ratio detection unit 182, an eye opening ratio feature extraction unit 183, an eye blink detection unit 184, an eye blink feature extraction unit 185, a head position detection unit 186, a head position feature extraction unit 187, a respiratory sound detection unit 188, a respiration feature extraction unit 189, a heart rate fluctuation detection unit 190, a heart rate fluctuation feature extraction unit 191, a blood pressure fluctuation detection unit 192, a blood pressure fluctuation feature extraction unit 193, a body movement detection unit 194, and a body movement feature extraction unit 195.

[0097] For example, the physiological feature extraction unit 171 uses the face image extraction unit 181 to the head position feature extraction unit 187 and may thereby extract an eye opening ratio feature, an eye blink feature, or a head position feature, which is related to the visual system physiological information of the driver, from an image which is photographed by the in-vehicle camera 161 and includes the driver, as the physiological information feature. Specifically, the face image extraction unit 181 consecutively acquires the image data from the in-vehicle camera 161 and extracts a face image region of the driver from the image data. The eye opening ratio detection unit 182 extracts the image region of the eye from the face image region extracted by the face image extraction unit 181, thereafter detects the upper and lower eyelids, and detects the eye opening ratio that indicates the opening degree of the eyelids, that is, the eye from the shapes of the eyelids. The eye opening ratio feature extraction unit 183 calculates an eye opening ratio feature, which is the average value, standard deviation, maximum value, minimum value, or the like of the eye opening ratio in a prescribed unit time, from the fluctuation pattern in the eye opening ratio that is detected by the eye opening ratio detection unit 182. Further, the eye blink detection unit 184 extracts the image region of the eye from the face image region extracted by the face image extraction unit 181, thereafter detects the upper and lower eyelids, and detects a blink (eye blink) from the movement of the upper and lower eyelids. The eye blink feature extraction unit 185 calculates an eye blink feature, which is the average value, standard deviation, or the like of the eye blink frequency or the eye blink time in a prescribed unit time. In addition, the head position detection unit 186 detects the fluctuation pattern in the head position of the driver based on the position in the image in the face image region extracted by the face i

SYSTEM AND METHOD FOR ASSESSING AROUSAL LEVEL OF DRIVER OF VEHICLE THAT CAN SELECT MANUAL DRIVING MODE OR AUTOMATED DRIVING MODE
An apparatus assesses an arousal level of a driver of a vehicle. The apparatus includes a processor and a memory storing a computer program, which when executed by the processor, causes the processor to perform operations. The operations include acquiring personal data of the driver, which are detected by one or more sensors when the vehicle is traveling. The operations also include developing, by machine learning with the acquired personal data, a model for estimating an arousal level to personalize the model to the driver, and determining the arousal level of the driver based on the personalized model.

[0022] However, there is a problem in that sufficient precision may not be obtained because in general the physiological information widely varies due to individual differences. For example, in the method disclosed in Japanese Unexamined Patent Application Publication No. 2014-181020 , the sleepiness of the driver is estimated to become higher, that is, the arousal level is estimated to become lower as the eye opening ratio, which is the opening magnitude of the eye of the driver, becomes lower. However, because the driver who habitually opens his/her eye widely and the driver who does not are present, the arousal level of all the drivers may not be estimated precisely based on the eye opening ratio that uses the same reference. Similarly, in the method disclosed in Japanese Unexamined Patent Application Publication No. 2016-38768 , the arousal level is estimated by using the heart rate information or the brain waves of the driver. However, the precision of those pieces of physiological information related to the autonomic nervous system and the central nervous system more widely varies due to individual differences than the eye opening ratio. Accordingly, in order to precisely estimate the arousal level by using the physiological information of the driver, an estimation method that absorbs variations of the physiological information due to individual differences is requested.

[0064] As illustrated in Fig. 1 , the arousal level estimation device 10 includes the vehicle behavior detection unit 11, the first arousal level recognition unit 12, the arousal level estimation unit 15, the physiological information detection unit 16, and the second arousal level recognition unit 17. Note that the arousal level (sleepiness) of the driver that is estimated by the arousal level estimation device 10 is decided as any one of five levels of level 1 to level 5, for example. For example, level 1 indicates "not sleepy state", and level 2 indicates "slightly sleepy state". Level 3 indicates "sleepy state", and level 4 indicates "fairly sleepy state". Level 5 indicates "very sleepy state".
https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2020054264A1&KC=A1&FT=D&ND=3&date=20200220&DB=&locale=en_EP#

VEHICLE CABIN MONITORING SYSTEM
A system and method for calibrating a vision system includes a selective wavelength interactive material located within the cabin of the vehicle and a control system in communication with the camera. The material is arranged as a pattern having pattern elements configured to reflect or absorb infrared light from an infrared light source. The control system is configured compare a captured image from the vision system that shows the pattern elements and calibrate the vision system based on a comparison of the captured image. Additionally or alternatively, the selective wavelength interactive material is disposed on a surface of a safety belt and the control system is configured to determine that an occupant seated in a seat is wearing a safety belt associated with the seat when the captured images show that the selective wavelength interactive material on the surface of the safety belt extending across the occupant seated.
https://worldwide.espacenet.com/publicationDetails/biblio?II=5&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200220&CC=WO&NR=2020037179A1&KC=A1#
http://www.freepatentsonline.com/y2020/0055480.html

Immonoloply?

Annika FL Larsson@AnnikaFLarsson @Veoneer
There are loads of things that can be achieved with the technology we have today. But it needs to be everywhere, for everyone!

Etienne Krug@etiennekrug @WHO
My message in #RoadSafetyConference2020 We’re at a crossroads. Either we wait 3-4 decades for new technologies seeing another 50 million road deaths = equivalent of WW1 or Spanish flu.
Or act on what we know works &move from dirty, passive &unsafe transport to clean, active &safe

OOIDA backs Missouri bill prohibiting driverless trucks
A Missouri state lawmaker wants to prohibit driverless truck operations from using roadways around the state.
Sponsored by Rep. Mike Moon, R-Ash Grove, the bill would require a trained person to be present in an autonomous vehicle.
Specifically, the legislation would forbid vehicles from being operated in automatic mode on a highway or street unless a licensed, trained person is present in the vehicle who has the ability to monitor the vehicle’s performance and take control of the vehicle if necessary.
Moon’s bill, HB2059, awaits assignment to committee.
https://landline.media/ooida-backs-missouri-bill-prohibiting-driverless-trucks/

Semicast Research@semicast_res
Hi
@ntsb
Would you kindly bring this excellent @NHVR report to the attention of Chairman Sumwalt? Fatigue & Distraction Detection Technologies (FDDTs) will address NTSB most wanted list items related to reducing fatigue-related accidents across multiple transport domains. Thanks.
https://www.nhvr.gov.au/files/202002-1120-phase-2-fatigue-distraction-detection-technology-australia.pdf

Semicast Research@semicast_res
Thought this NHVR report might interest you
@danielmhinklemany ways to improve road safety before AVs.

Seeing Machines@seeingmachines
Nobody tells our story better than the heavy vehicle industry itself. @seeingmachines
#Guardian is setting 'an industry standard' when it comes to #fatigue and #distraction technology use, according to some of the biggest names in the industry.
@NHVR

https://twitter.com/seeingmachines/status/1229603718812688386

May have posted previously . Posted again as directly related to post below.
DRIVING ASSISTANCE SYSTEM
A driving assistance system includes a driving readiness degree estimation unit configured to estimate a driving readiness degree relating to a driving consciousness of the driver based on the travel state of the vehicle or the driving operation of the vehicle by the driver and the traveling environment of the vehicle, a proportional gain calculation unit configured to calculate a proportional gain based on the driving readiness degree and the speed of the vehicle, and an assistance torque calculation unit configured to calculate the assistance torque according to a value obtained by multiplying a difference between the target steering angle and the actual steering angle by the proportional gain. If the speed is constant, the proportional gain calculation unit is configured to calculate the proportional gain as a smaller value as the driving readiness degree becomes lower.
https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2020017111A1&KC=A1&FT=D&ND=3&date=20200116&DB=&locale=en_EP#

[0054] The inattention time recognition unit 15 recognizes a time duration of eye glance away from road environment (inattention time) which is a time the inattention of the driver continues based on the driver image acquired by the driver image acquisition unit 11, for example. The method of recognizing the inattention time is not particularly limited, and various well-known methods can be adopted.

[0055] The arousal level recognition unit 16 recognizes the arousal level of the driver from the eye closing rate of the driver per minute, an eye open situation, a blinking frequency, or the movement of eyeballs, and the like, based on the driver image acquired by the driver image acquisition unit 11. The arousal level recognition unit 16 may recognize the arousal level of the driver based on the state of the host vehicle recognized by the vehicle state recognition unit 13. The arousal level is a degree indicating that the consciousness of the driver is not in a state of being hazy due to lack of sleep or the like, but in an awakening state. The method of recognizing the arousal level is not particularly limited, and various well-known methods can be adopted.

[0056] The driving readiness degree estimation unit 17 estimates the driving readiness degree based on the reaction time recognized by the reaction time recognition unit 14, the inattention time recognized by the inattention time recognition unit 15, and the arousal level recognized by the arousal level recognition unit 16. The driving readiness degree is a degree according to the height of the driving consciousness of the driver. The driving consciousness is a generic term for mental activities including cognition, prediction, determination, and an operation when the driver drives a vehicle.

[0057] The driving readiness degree is a level of readiness for the driving by the driver. The driving readiness degree is also called “readiness”. As the driving consciousness and the degree of readiness of the driver against the traveling environment of the host vehicle increase, the value of driving readiness degree increases. As an example, the driving readiness degree here can be divided into five stages such as Low (low), Medium Low (slightly low), Medium (normal), Medium High (slightly high) and High (high). The way of expressing the driving readiness degree is not particularly limited, and may be expressed by a numerical value or may be divided into plural stages.

[0007] In the driving consciousness estimation device according to an aspect of the present disclosure, the driving readiness degree estimation unit may be configured to estimate the driving readiness degree based on the width of the field of view and at least one of the time duration of eye glance away from road environment and the arousal level, if the driver is driving the vehicle with manual driving. At the time of manual driving in which it is difficult to appropriately determine the reaction of the driver, it is possible to appropriately estimate the driving readiness degree based on the width of the field of view of the driver.

[0034] A driver monitor camera 1, an external sensor 2, an internal sensor 3, a global positioning system (GPS) receiver 4, a map database 5, and an HMI 6 are connected to the ECU 10. The ECU 10 is connected to an autonomous drive ECU 7.

[0035] The driver monitor camera 1 is provided on a cover of a steering column of the host vehicle and in front of the driver, and images a head of the driver. Since the driver monitor camera 1 images the driver from plural directions, plural number of cameras may be provided. The driver monitor camera 1 transmits a driver image in which the driver is imaged to the ECU 10.

[0052] The reaction time recognition unit 14 recognizes a reaction time (a reaction delay) of the driver. The reaction time includes a peripheral reaction time and a vehicle behavior reaction time. The reaction time recognition unit 14 recognizes the reaction time from the timing when the viewing target is seen by the driver to the timing when the driver visually recognizes the viewing target as the peripheral vision reaction time. The reaction time recognition unit 14 recognizes the reaction time from the timing when the viewing target is seen by the driver to the timing when the driver executes the vehicle behavior against the viewing target as the vehicle behavior reaction time. The method of recognizing the reaction time is not particularly limited, and various well-known methods can be adopted.

[0053] The viewing target can be detected from the traveling environment recognized by the traveling environment recognition unit 12. The viewing target includes at least one of a surrounding vehicle, a road shape, and an installed object on the road. A line of sight of the driver can be detected from the driver image acquired by the driver image acquisition unit 11. The line of sight of the driver can be detected by detecting eyeballs and movements of the eyeballs of the driver from the driver image using a well-known method. The vehicle behavior can be detected from the state of the host vehicle recognized by the vehicle state recognition unit 13.

DRIVING CONSCIOUSNESS ESTIMATION DEVICE
A driving consciousness estimation device includes a driving readiness degree estimation unit configured to estimate a driving readiness degree relating to a driving consciousness of a driver based on a reaction time or a width of field of view of the driver and at least one of a time duration of eye glance away from road environment and an arousal level of the driver. An influence of the reaction time or the width of the field of view on the driving readiness degree estimated by the driving readiness degree estimation unit is greater than an influence of at least one of the time duration of eye glance away from road environment and the arousal level on the driving readiness degree.

[0005] A driving consciousness estimation device according to an aspect of the present disclosure includes a driving readiness degree estimation unit configured to estimate a driving readiness degree relating to a driving consciousness of a driver based on a reaction time or a width of field of view of the driver of a vehicle and at least one of a time duration of eye glance away from road environment and an arousal level of the driver. An influence of the reaction time or the width of the field of view on the driving readiness degree estimated by the driving readiness degree estimation unit is greater than an influence of at least one of the time duration of eye glance away from road environment and the arousal level on the driving readiness degree.

[0006] It can be seen that the reaction time or the width of the field of view of the driver further contributes to the driving safety than the time duration of eye glance away from road environment and the arousal level of the driver. Therefore, in the driving consciousness estimation device according to the aspect of the present disclosure, the influence of the reaction time or the width of the field of view on the driving readiness degree is set greater than the influence of at least one of the time duration of eye glance away from road environment and the arousal level. As a result, it is possible to estimate the driving readiness degree with sufficient consideration of the driving safety, and thus, it is possible to improve the driving safety.
https://worldwide.espacenet.com/publicationDetails/biblio?II=0&ND=3&adjacent=true&locale=en_EP&FT=D&date=20200213&CC=US&NR=2020047765A1&KC=A1#

COMPACT CAMERA MODULE FOR OCCUPANT MONITORING SYSTEM
A camera module for an occupant monitoring system is disclosed. The camera module includes a housing with an interior chamber and an aperture on one surface. An image sensor is position within the chamber and aligned with the aperture. A lens is positioned between the image sensor and the aperture along an optical axis to focus light entering the aperture onto the image sensor. To reflect light into the aperture from a field of view that is not along the optical axis, the camera module includes a planar reflector that extends at an angle from the surface of the housing and over the aperture.
https://worldwide.espacenet.com/publicationDetails/biblio?CC=DE&NR=112018000331T5&KC=T5&FT=D&ND=3&date=20200123&DB=&locale=en_EP#

The description from Qualcomm and the LG patent look very similar to me a totally technically unqualified observer;

LG ELECTRONICS KR 2020-02-06
XR DEVICE AND METHOD FOR CONTROLLING THE SAME
An extended reality (XR) device and a method for controlling the same are disclosed. The XR device is applicable to 5G communication technology, robot technology, autonomous driving technology, and Artificial Intelligence (AI) technology. The XR device includes a transparent display, a sensing unit configured to sense a relative position and gaze direction of a user with respect to the transparent display, and a processor configured to recognize at least one real-world external object that is located in a forward direction of the transparent display and is visible to the user through the transparent display, based on the relative position and gaze direction of the user sensed by the sensing unit.
https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2020043239A1&KC=A1&FT=D&ND=3&date=20200206&DB=&locale=en_EP#

Qualcomm 2020-01-30
ENABLING AUGMENTED REALITY USING EYE GAZE TRACKING
Methods and apparatus relating to enabling augmented reality applications using eye gaze tracking are disclosed. An exemplary method according to the disclosure includes displaying an image to a user of a scene viewable by the user, receiving information indicative of an eye gaze of the user, determining an area of interest within the image based on the eye gaze information, determining an image segment based on the area of interest, initiating an object recognition process on the image segment, and displaying results of the object recognition process.
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2020033943A1&KC=A1&FT=D&ND=3&date=20200130&DB=&locale=en_EP#