Augmented reality on the road v7
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Augmented Reality on the Road - presented at Augmented Planet's 2014 conference on Nov 17 An introduction to the volumetric 3D Head Up Display from Making Virtual Solid, LLC, - for cars and other road vehicles, and how it might be used to improve driver awareness and road safety (i.e. ADAS).
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Augmented reality on the road v7
- 1. Throwing Markers Outside the Car Marianne Lindsell Advisor to Making Virtual Solid, LLC Disclaimer: Information in these slides is representative of the True3D technology of Making Virtual Solid, LLC. There remains some flexibility in actual design to meet the requirements of individual clients. These slides also incorporate the research and opinions of the author at the time of writing. The author takes no responsibility for the accuracy or use of the information contained here. © Marianne Lindsell 2014. All rights reserved. Slide 1
- 2. 1. My Background and approach to AR 2. The True3DTM HUD from Making Virtual Solid, LLC 3. Using it to improve Situational Awareness 4. A Model for Task-focussed AR? © Marianne Lindsell 2014. All rights reserved. 17:54 Slide 2
- 3. See against Bright Light Swap Senses Show Pulse Guidance & Navigation Skin Circulation Add Heat Map Remove Patterns Mark in Avatar-mediated Comms Facial Recognition Places Things People Lines, Areas, Volumes Marker Triggered Visualise a volume Remote Skilling Markers & Tracking Object / Image Triggered 3D Maintenance Overlay Earlier Structure Space Visualise Enhance Senses Enhance Context See-through solid objects, e.g. walls (camera view or graphics Work with Faces Virtual Surroundings Future Structure for the Airborne Camera Straw based on known data) © Marianne Lindsell 2014. All rights reserved. 17:54 Slide 3
- 4. Visuo-spatial sketch pad Assumed to be the system that handles attention Phonological Loop Central executive Experiments show people can recall 4 or 5 items immediately but recall rate drops very quickly after a few seconds Like a continuous audio tape of 2 seconds Based on: Alan D. Baddeley, Essentials of Human Memory, Psychology Press 1999 © Marianne Lindsell 2014. All rights reserved. 17:54 Slide 4
- 5. • Allow a clear view and not ‘get in the way’ • Enhance surroundings rather than FoV • i.e. Conformal Representation (as per NASA Ames/SJSU taxiing studies with untrained crew) • Remove enhancements when no longer needed © Marianne Lindsell 2014. All rights reserved. 17:54 Slide 5
- 6. Many planar cross-sections of a 3D volume are projected onto a spinning diffuser in this Perspecta volumetric 3D display. In this example it creates a volume-filling image of a scan for brain cancer. © Marianne Lindsell 2014. All rights reserved. 17:54 Slide 6
- 7. 18 degrees 20 degrees © Marianne Lindsell 2014. All rights reserved. 17:54 Slide 7
- 8. 1. Sun filter 2. Eye lens group 3. Folding mirror(s) 4. Relay lens group 5. Projection screen 5. a. Path of laser spot 6. Projection screen actuator 7. Laser beam 8. Scanner 9. Beam focuser and profiler 10. Laser 11. Circuit board 12. RGB raster projector NOTE: Objects not drawn to scale © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 8
- 9. Power consumption in region of 20W, increasing or decreasing according to requirement and usage Installation volume is around 7 litres, with much flexibility in the shape © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 9
- 10. MVS won European Satellite Navigation Competition 2011 The Virtual CableTM is shown 8 deg above path being tracked, as per NASA Ames/SJSU studies But navigation is not top priority (for automotive OEMS): 1) ADAS (Advanced Driver Assistance Systems) – mainly safety 2) Navigation 3) Heads Up Vehicle Management (e.g. Controls, Aircon, Entertainment) © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 10
- 11. Looked-But-Failed-To-See Crashes collisions in which a driver fails to detect another road user or object despite looking at it Third most frequently occurring driver error (Brown, 2005; Treat, 1980; White & Caird, 2010) Why do LBFTS crashes occur? Inattentional Blindness & Change Blindness (AKA cognitive tunnelling, cognitive capture, channelised attention) The failure to see highly visible objects we may be looking at directly when our (Mack & Rock, 1998) attention is elsewhere. After - Does Familiarity breed inattention? Why drivers crash on the roads they know best Samuel G. Charlton, Traffic & Road Safety Research Group, Waikato University © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 11
- 12. Images, Car and Driver magazine © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 12
- 13. *Mica Endsley’s 3 levels of Situational Awareness: * Mica Endsley is presently Chief Scientist of the U.S. Air Force Situational Awareness is quite important 1. Perceive items around you e.g. spotting a road sign 2. Comprehend their meaning i.e. understanding it is a warning, e.g. child symbol outside a school - so I can expect children crossing 3. Project their status into near future e.g. I’m driving at 40 mph, so I’d better slow down in case a child runs out in front of me Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32- 64. Endsley, M. R. (1995). Measurement of situation awareness in dynamic systems. Human Factors, 37(1), 65-84. © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 13
- 14. A clever scheme for Level 2 SA Mark traffic features for Level 1 SA Perceive items around you Comprehend their meaning Traffic Vectors? Show play-out markers for Level 3 SA Project status into near future Already some OEMs are simulating warnings of shunt risk As we realise V2V comms, could we show likely paths of other vehicles? • Joining from a hidden entrance? • Cutting across lanes near us? • Approaching us at junctions? Surprisingly, visual AR compares favourably with LIDAR and Radar © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 14
- 15. Embedded Mobile • Compact • Available (e.g. driver’s cellphone, with potential to restrict distracting SMS) • High processing power • Extensive storage • Updateable online • Distributed – allowing for preprocessors (e.g. camera DSPs) • Deterministic RTOS • More reliable for ADAS • QNX • Automotive Linux • Mentor Nucleus • Android Auto • Apple CarPlay • Mirrorlink • GENIVI® But only for infotainment (IVI) and navigation © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 15
- 16. Automation Human Driver Faster React to unexpected More Accurate Interpret response in keeping with social principles More Reliable Respond better to other humans Condenses know-how of many humans Will still be driving for a long time Embedded ADAS tending toward autonomous control e.g. auto-brake But once control ripped from driver, difficult to return it Unless driver SA can be sustained Can AR help the driver stay aware of the automation? © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 16
- 17. • A sort of early HUD • AR was originally a work horse Picture, Geoff Adams, NRLCEO fantasy rugby league The WWII Reflector Sight Picture, Wikipedia Moi? Fighter HUD <> Car HUD <> Smart/AR Glasses But there is arguably a common thread: • Minimise clutter & obstruction • Maximise situational awareness © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 17
- 18. © Marianne Lindsell 2014. All rights reserved. Slide 18
- 19. © Marianne Lindsell 2014. All rights reserved. Slide 19
- 20. © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 20
- 21. AR Aerial Video Context Mapping System The Churchill Augmented Reality mapping system, ARS, overlays street and other information directly over live sensor video from aircraft-mounted gyro-stabilized cameras such as the FLIR Star Safire and L- 3 Wescam MX series gimbals. MGRS grid lines, radar targets and much more can also be turned on or off at will -- eliminating the need to take eyes off the target, and radically enhancing the ability of sensor operators to quickly and accurately direct ground assets to where they are needed. Get the most out of your turret by interfacing it to an ARS system for an immediate improvement in utility and accuracy. Note registration between sensor video and VR map surround © Marianne Lindsell 2014. All rights reserved. 17:55 Slide 21
Editor's Notes
- I find this a fascinating area, as to some extent it cuts to the root of the role that machines can play in facilitating human activities, so please forgive a bit of a ramble . . As a general point, I am not so surprised that automation works best when workload is light and the task routine, as it seems to me that we give too little emphasis to evaluating where and how automation will be effective in the first place. We seem to have a collective expectation that machines will ultimately be able to replace almost every and any human activity, when in reality machines and human beings differ in their strengths and weaknesses. Indeed automation could be viewed as simply the delegation of human activities to an external system, to free up capacity in our higher thought processes, much as the cerebral hemispheres delegate to the cerebellum, except that 'artificial' automation can extend to new sensors and 'actors' as well. Automation is often faster, more accurate and more reliable (repetitive) than humans at making logical evaluations and inferences where detailed procedures and guidelines (automation protocols) have been provided (by human beings). That the human beings are required to provide the automation protocols in the first place, also has an important benefit, - that a single instance of automation can articulate the collective knowledge, know-how and design efforts of a wide base of subject matter experts. The addition of speed, accuracy and reliability to this makes for a potent combination. One should also keep in mind the immense humanitarian, ethical, economic, risk and engineering benefits in excluding human beings from some activities. However, I submit that humans remain more effective than machines at reacting to the unexpected in keeping with the common values and priorities of humankind (or indeed of social groups, including formalised command structures). These values and priorities can be complex and the ability to articulate them quickly and correctly may require substantial training, and even if it doesn't, it may lean on the psychosocial processes of 'growing up' over time within a particular social or cultural setting. It is very difficult to confer these abilities onto machines, not least because human beings have a variety of indirect ways of applying them, including the accumulation over time of intuition, and the use of affective states (emotions). These latter points also give humans an obvious advantage over machines in relating to other humans. However rigorous the preparation, design and assurance, the very nature of entropy means that events will always be able to take any of an undefinable number of unexpected turns. We try to anticipate and accommodate those eventualities we have experienced (by design), and even try to add in provision for those we can only imagine (usually by generalising from related experiences, but also to some extent by abstract thought), but we can never cover all possibilities. This makes human judgement, initiative and performance under pressure impossible to automate completely. Therefore, in many cases, we may have more success in teaming humans with machines, than in replacing humans with machines; i.e. partial automation rather than total automation.