Vehicular Visible Light Communications

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Mobile and Vehicular Network Lab

Vehicular

Visible Light Communications

Hsin-Mu (Michael) Tsai (蔡欣穆)

Computer Science and Information Engineering National Taiwan University

2014 APEC Cooperative Forum on Internet of Vehicles

2014/5/20

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Mobile and Vehicular Network Lab

http://www.forcegt.com/wp-­‐content/uploads/2012/09/volvo_road_train-­‐11.jpg 2

IoV: Cooperation between Vehicles 

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   2

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Mobile and Vehicular Network Lab

Building Roads: No Moore’s Law Here

Wugu-Yangmei Overpass (completed in 2013) 
 (for Freeway No. 1 in Taiwan)"

–  Cost: 3B USD for 40 km of elevated road


- The most expensive (per unit length) in the history"

–  Divert approximately 25% of traffic from the original highway"

–  Improve rush hour average speed: 


- 40-50 kmph à 80-90 kmph"

http://twimg.edgesuite.net//images/twapple/640pix/20121217/LN06/LN06_004.jpg

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Hsin-Mu Tsai (c) 2010-2014 All rights reserved.

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Mobile and Vehicular Network Lab

Increase Road and Energy Efficiency 

•  At 100 km/h, the road surface utilization = 5%!

•  Reduce the gap - Longitudinal control 


" " " " "(adaptive cruise control) !

•  Reduce lane width - Automatic steering"

•  Reduce aerodynamic drag - Close-formation

automated platoons can save 10% to 20% of total energy use"

•  Only possible through cooperation with communications!

–  Drivers’ response delays cause stop and go disturbances"

45.5  m

5  m

3. 5   m  1.8  m 

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Hsin-Mu Tsai (c) 2010-2014 All rights reserved.

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Mobile and Vehicular Network Lab

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Challenges: V2V Communications 

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Mobile and Vehicular Network Lab

Challenge 1: Density and Speed 

•  Range à reaction time"

•  200 km/h relative speed


100 m = 1.8 s @ 200 km/h"

•  A 100-m road segment can have:"

– More than 600 scooters"

– More than 120 cars"

•  Heavy Interference"

•  The need for 


reliable communications"

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Hsin-Mu Tsai (c) 2010-2014 All rights reserved.

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Mobile and Vehicular Network Lab

Challenge 2: 

My  loca(on  is  (X3,  Y3)  and  my   speed  is  98  mph,  and  I  am  braking.

My  loca(on  is  (X2,  Y2)  and   my  speed  is  101  mph.

My  loca(on  is  (X1,  Y1)  and   my  speed  is  106  mph.

GPS  Uncertainty  =  5  -­‐10  m

Who is Speaking?

Is the car in front of me braking?

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   7

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Mobile and Vehicular Network Lab

Challenge 3: Cost and Benefit 

•  DSRC (IEEE 802.11p) – the current dominant standard for vehicular communications"

•  NOT in vehicle products today"

•  Low adoption incentive: 


where is the (day 1) benefit?!

– Minimum market penetration rate: 10%


(All new cars have it à have to wait for 2 years)"

•  Low adoption incentive: high initial cost!

– Complexity similar to a WiFi radio"

– But with much lower quantity, 


when bootstrapping the market"

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   8

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Mobile and Vehicular Network Lab

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Vehicular

Visible Light Communications

EE  Times:  Top  10  Automotive  Agendas  for  2014  &  Beyond   7.  Time  to  act  on  V2V,V2I  

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   9

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Mobile and Vehicular Network Lab

Vehicular Visible Light Communications 

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1 0

1010

1      0      1      0

Time

Receiver  Transmitter 

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.  

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Mobile and Vehicular Network Lab

Keep the Light’s Original Function

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Human  eyes  can’t  perceive  LED’s  high  frequency  flickering        (persistence  of  vision)

Light

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Time

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.  

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Mobile and Vehicular Network Lab

Receiver Options

Data Rate Number

of pixels Cost

Photodiode

module Up to 


several Mb/s 1 Extra

Camera

sensor A few Bytes/s Millions Use

existing cameras

Photodiode module Camera Sensor

Transmitter LED Lights

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   12

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Mobile and Vehicular Network Lab

Video:  http://goo.gl/lo1f9 13

V2LC for Scooter Safety

S.-­‐H.  You,  S.-­‐H.  Chang,  H.-­‐M.  Lin,  H.-­‐M.  Tsai,  "Demo:  Visible  Light  

Communications  for  Scooter  Safety,“  ACM  MobiSys,  Taipei,  Taiwan,  June  2013 Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   13

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Mobile and Vehicular Network Lab

Your  Car

Automatic Interference Filtering 

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•  Visible light cannot penetrate obstacles = line-of-sight only"

•  VLC communication coverage = immediate neighbors only"

"à only those that could immediately collide with you!

•  Comparison: RF coverage includes a lot more irrelevant vehicles à additional interference à less reliable

RF  Coverage

VLC  Coverage

Automatic  interference  filtering  with  VLC     à  Zero  delay  +  zero  overhead  

à  

Always  scalable  to  density  &  speed*!

 

*S.-­‐H.  You,  O.  Shih,  N.  Wisitpongphan,  H.-­‐M.  Tsai,  and  R.  D.  Roberts,  “Smart  Automotive  Lighting  for  Vehicle  

Safety,”  IEEE  Communications,  vol.51,  no.12,  pp.  50-­‐59,  December  2013. Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.  

Challenge 1 

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Mobile and Vehicular Network Lab

Highly Directional Transmission 

Easily regulated output radiation pattern


àHighly directional transmission with minimal leakage"

x location (m)

y location (m)

0 2 4 6 8 10

0 1 2 3 4 5 6

-120 -100 -80

Scooter  Tail  Light   -60

Radiation  Pattern

Tà

Safety  regulation  specifies  the   minimum  light  intensity  (at   30.48  m)  within  20  degrees  of   irradiance  angle.

W.  Viriyasitavat,  S.-­‐H.  Yu,  and  H.-­‐M.  Tsai,  “Short  Paper:  Channel  Model  for  Visible  Light   Communications  using  Off-­‐the-­‐shelf  Scooter  Taillight,”  in  Proc.  IEEE  Vehicular  Networking  

Conference,  pp.  170-­‐173,  Boston,  United  States,  December  2013. Hsin-Mu Tsai (c) 2010-2014 All rights reserved. 15

Challenge 1 

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Mobile and Vehicular Network Lab

Simultaneous Transmissions 

•  Lights occupy disjoint sets of pixels 


" " "(except when very far away: > 1km)"

•  Simultaneous transmissions 


"with no collision, interference, and access delay"

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Hsin-Mu Tsai (c) 2010-2014 All rights reserved.

Challenge 1 

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Mobile and Vehicular Network Lab

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Talking Pixels 

•  Computer vision: 


associate pixels with information (distance) and context"

•  V2LC: direct association -


Information directly comes from the pixels!"

•  Photogrammetry: sub-meter accuracy for positioning"

www.ionroad.com

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Hsin-­‐Mu  Tsai  (c)   2010-­‐2014  All  rights   reserved.    

Challenge 2 

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Mobile and Vehicular Network Lab

•  Low Cost: LED lights already used extensively for vehicles"

•  Extremely simple driver circuit: USD$2 microcontroller"

•  Aftermarket product is feasible: 


" "driver module and/or light module"

Low-Cost Transmitter:

Pervasive Existence of Automotive LED lights 

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   18

Challenge 3 

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Mobile and Vehicular Network Lab

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Luxgen’s Eagle View+

360 degree vision coverage

•  Low cost: cameras already exist in many cars"

•  Aftermarket product is feasible: "

•  App download for mobile devices"

•  WiFi/Bluetooth link to light module"

" 19

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.  

Challenge 3 

Low-Cost Receiver:

Cameras Everywhere 

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Mobile and Vehicular Network Lab

Conclusion 

•  IoV  creates  new  opportunities  for  both  new  

applications  and  huge  performance  improvements  

•  New  challenges  need  to  be  addressed:  

–  Vehicle  density  and  speed  

–  Association  of  information  and  identity   –  Market  penetration  

•  V2LC:  new  solution  for  vehicular  communications  

–  Reliable  “point-­‐to-­‐point”  V2V  communications   –  Direct  association  of  information  and  pixels  

–  Low-­‐cost  system  design  with  aftermarket  product   options  

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   20

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Mobile and Vehicular Network Lab

Thank you! 

Please  feel  free  to  contact  me  for  questions:  

Prof.  Hsin-­‐Mu  (Michael)  Tsai  (蔡欣穆)  

hsinmu@csie.ntu.edu.tw  

http://www.csie.ntu.edu.tw/~hsinmu/  

 

Computer  Science  and  Information  Engineering   National  Taiwan  University 

Hsin-­‐Mu  Tsai  (c)  2010-­‐2014  All  rights  reserved.   21

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