Autonomous vehicles (AVs) represent one of the most promising new technologies for smart cities and for humans in general. The problem is that cities will not realize the full benefits from AVs until roads are designed for them. Until this occurs, their main benefit will be the elimination of the driver and steering wheel, which will reduce the cost and increase the capacity of taxis; but even this impact will not occur for many years because of safety concerns. Thus, in the near term, the main benefit of AVs will be free time for the driver to do emails and other smart phone related tasks. A better solution is to design roads for AVs or in other words, to constrain the environment for AVs in order to simplify the engineering problem for them. For example, designing roads so that all vehicles can be controlled by a combination of wireless communication, RFID tags, and magnets will reduce the cost of AVs and increase their benefits. Only AVs would be allowed on these roads, they are checked for autonomous capability at the entrance, and control is returned to the driver when an AV leaves the road. Existing cars can be retrofitted with wireless modules that enable cars to be controlled by a central system, thus enabling cars to travel closely together. The magnets and RFID tags create an invisible railway that keeps the AVs in their lanes while wireless communication is used for lane changing and exiting a highway (Chang et al, 2014; Le Quesne et al, 2014). These wireless modules, magnets and RFID tags will be much cheaper than the expensive LIDAR that is needed when AVs are mixed with conventional vehicles on a road. The benefits from dedicating roads to AVs include higher vehicle densities, less congestion, faster travel times, and higher fuel efficiencies. These seemingly contradicting goals can be achieved because AVs can have shorter inter-vehicle distances even at high speeds thus enabling higher densities, lower congestion, and lower travel times. The less congestion and thus fewer instances of slow moving or stopped vehicles enable the vehicles to travel at those speeds at which higher fuel efficiencies can be achieved (Funk, 2015). In combination with new forms of multiple passenger ride sharing, the higher fuel efficiencies will also reduce carbon emissions and thus help fight climate change. The challenge is to develop a robust system that can be easily deployed in various cities and that will be compatible with vehicles containing the proper subsystems. Such a system can be developed in much the same way that new cellular systems are developed and tested. Suppliers of mobile phone infrastructure, automobiles, sensors, LIDAR, 3D vision systems, and other components must work with city governments and universities to develop and test a robust architecture followed by the development of a detail design.

1. Jeffrey Funk
Retired from
National University of Singapore,
Hitotsubashi University, Kobe University, Penn
State, Carnegie Mellon, University of Michigan
For information on other technologies, see http://www.slideshare.net/Funk98/presentations

2. The First Cars were Implemented in a
Constrained Environment
 Paved roads were created for
autos
 Highways were created for fast
moving autos
 Special entry points
 Horses, bicycles, and old
vehicles aren’t allowed
 Fences prevent entry by animals
and children at other points
 These paved roads and high-
ways reduce complexity of
driving and thus increase safety

3. Other Technologies also Implemented in
Constrained Environment
 Planes use airports and
special flight corridors
 Ships uses ports and special
corridors within ports
 IT uses standards to simplify
design
 Interface standards exist for
most products
 Compatibility may emerge later
(e.g., Wintel and Apple
computers)

4. Shouldn’t We “Constrain” the
Environment for Driverless Vehicles?
 Won’t allowing them on all
roads and all parking lots
be dangerous?
 Without constraints, AVs
must handle many
contingencies
 Children run onto road
 Cars run out of gas or break
down
 Street or traffic lights stop
working
 Chaos of parking lots

5. Bad or Unusual Weather Provides Other
Reasons for Constraints
 Difficult situations
 Dark, Raining
 Snowing
 Foggy, Windy
 It will take many
years for driverless
vehicles to handle
all situations
 Would you drive
next to driverless
truck on snowy
day?

6. Without Constraints, the Benefits
from AVs are very Small
 Drivers can do something else
while AV is self-driving
 Read, watch videos
 Is this a large benefit?
 Governments may allow driver to
be eliminated
 Reduces cost of taxis
 Increases capacity of taxis
 Is this a large benefit and when
might governments allow these
changes?
 Shouldn’t we be looking for bigger
benefits?

7. Shouldn’t we be Looking for Larger Benefits
 Can we move these vehicles at
60 MPH?
 Reducing travel time is
potentially big benefit
 When roads are completely
filled with driverless vehicles
 Inter-vehicle distances can be
reduced
 Traffic signals can be
eliminated
 Both enable higher capacity
roads, perhaps enabling roads
to be used for something else
 25% of space in Los Angeles is
for roads and parking lots

8. City Percentage
Devoted to Streets
Street Area (square feet)
Per Capita
New York 30% 345
Newark 16% 257
San Francisco 26% 441
Chicago 24% 424
Philadelphia 19% 365
St. Louis 25% 609
Pittsburgh 18% 455
Cleveland 17% 416
Miami 24% 778
Milwaukee 20% 724
Cincinnati 13% 573
Los Angeles 14% 741
Atlanta 15% 1,120
Houston 13% 1.585
Dallas 13% 1,575
Portion of Land Devoted to Streets
Source: John R. Meyer and Jose A. Gomez-Ibanez, Autos, Transit, and Cities, Twentieth
Century Fund Report (Cambridge: Harvard University Press, 1981).

9. Rank City Parking Area* Divided by Land
Area
1 Los Angeles 81%
2 Melbourne 76%
3 Adelaide 73%
4 Houston 57%
5 Detroit 56%
6 Washington, D.C. 54%
7 Brisbane 52%
8 Calgary 47%
9 Portland 46%
10 Brussels 45%
Land for Parking in Urban Areas
Source: Michael Manville and Donald Shoup, “People, Parking, and Cities,” Journal of Urban Planning and Development, Vol.
131, No. 4, December 2005, pp. 233-245
* Includes all levels of all parking garages

10. The Bottom Line
 Safety problems are large as long as both AVs and
conventional vehicles are interacting on roads and in
parking lots
 Elimination of driver and driver’s seat is small benefit
 The benefits from driverless vehicles don’t become large
until all vehicles on a road (or lane of road) are driverless
vehicles
 This should be the goal of driverless vehicles
 Cities can charge users for access to roads (or lanes)
dedicated to AVs
 New revenue source for cities, which can be used for many
things
 Constraining the environment can increase safety and
reduce the cost of the vehicles

11. What Might These “Autonomous Roads” (or
Lanes in Roads) be Like?
 Vehicles are Controlled by Wireless
Communication Technologies on Dedicated Roads
 Cars are checked for autonomous capability when
they enter a dedicated road
 Route plans are checked and integrated with other
route plans
 Improvements in computer processing power
facilitate checking and integrating
 Much of these calculations would be done in
secure private cloud

12. Other Simple Solutions that
Provide Additional Safety
 Magnets and RFID tags can
be embedded in highways to
help control vehicles
 They create an invisible
railway
 Estimated cost in Singapore
 <200M SGD for magnets
 <110M SGD for RFID
 Very cheap, less than 2SGD
per vehicle

13. Dedicated Roads Lead to Higher Capacity Roads

14. Dedicated Roads Lead to Fewer Delays at Traffic Signals

15. Roads dedicated to AVs can have higher speeds and
thus higher Fuel Efficiencies (lower carbon emissions)
Can we move these
cars at 30MPH or faster?

16. Latency is Key Issue but it is Still Falling
 Expected to fall below 0.1 milliseconds with
wireless 5G services that will be implemented by
early 2020s
 Jones R 2015. Telecom’s Next Goal: Defining 5G, Wall Street Journal, March 9.
http://www.wsj.com/articles/telecom-industry-bets-on-5g-1425895320
 Could AVs become the main market for cellular
5G services?
 Processing is done in cloud and the cost of these
cloud services continues to fall
 Falling latency requires better IT, but this keeps
occurring through Moore’s Law

17. Improvements in Latency (delay times in
milliseconds) Enable Centralized Control of Vehicles

18. High Processing Capability is Needed to Control Vehicles
Improvements in Integrated Circuits and Computers Enable this Processing Power
Processing power for 100 km road by vehicle inflow and reaction times
(Several thousands PCs)

19. Many of the Computer Calculations (price per car)
Would be Done in the Cloud

20. Moore’s Law Drives Reductions in Cloud
Computing Services (price per car)

21. Let’s Design “Autonomous Roads” for AVs
 Dedicate roads or lanes in roads to AVs
 Over time increase number of roads (or lanes) that are
dedicated to AVs
 This would
 Increase safety of AVs, while increasing benefits from AVs
 And reducing cost of AVs
 Cost of AVs is already falling rapidly (see subsequent slides)
 Emphasizing wireless control will reduce necessary on-car
capabilities and thus cost of AVs
 <$5,000 per car is possible
 Capabilities can be embedded in module that can be added to
existing vehicles

22. Begin with Highways
 Benefit from
higher density of
cars per area, all
fast moving
 Eliminate some
highways (or
lanes) since
autonomous
highways have
more capacity

23. Then Transform Surface Streets
 Higher capacity of
autonomous roads
enables some roads
to be used for other
purposes
 Autonomous roads
can be surrounded
by fences and
perhaps roofs, thus
enabling parks or
other facilities to be
constructed on top of
them

24. Cost of Autonomous Vehicles (Google Car) Falls as Improvements
in Lasers and Other “Components” Occur
Source: Wired Magazine, http://www.wired.com/magazine/2012/01/ff_autonomouscars/3/

25. Better Lasers, Camera chips, MEMS, ICs, GPS Are Making these
Vehicles Economically Feasible 1 Radar: triggers alert when something
is in blind spot
2 Lane-keeping: Cameras recognize lane
markings by spotting contrast between road
surface and boundary lines
3 LIDAR: Light Detection and Ranging
system depends on 64 lasers, spinning at
upwards of 900 rpm, to generate a 360-
degree view
4 Infrared Camera: camera detects
objects
5 Stereo Vision: two cameras build a
real-time 3-D image of the road ahead
6 GPS/Inertial Measurement: tells us
location on map
7 Wheel Encoder: wheel-mounted
sensors measure wheel velocity
ICs interpret and act on this data

26. Falling Cost of Autonomous Vehicles
 Cost of “Google Car” was $150,000 in 2012
 mostly for electronic components
 about $70,000 for LIDAR from Velodyne
 Current rates of improvement are 30%-40%
 If costs drop 25% a year, cost of electronics will drop by 90%
in ten years
 May be evolutionary move towards AVs as Sensors are
incorporated into existing vehicles http://www.ti.com/ww/en/analog/car-of-
the-future/?DCMP=gma-tra-carofthefuture-en&HQS=carofthefuture-bs-en
 But many of these costs have dropped faster than this
calculation
 Velodyne offers low-cost LIDAR for $8,000
http://www.theguardian.com/technology/2013/jun/02/autonomous-cars-expensive-google-
http://www.wsj.com/articles/continental-buys-sensor-technology-for-self-driving-cars-1457042039

27. Cost of Self-Driving Car Feature Self-Driving Car Volume Forecast
Other Cost (and Volume) Estimates for AVs
• Cost is key hurdle of Google’s self driving car
• Cost ~ $200,000 to build in 2014
• By 2015, cost reduced to $50,000
• Further reduction as technology matures and volume increase
• Look out for cost to reach $7000. Will lead to rapid adoption

28. Wireless Control Enables Much Cheaper AVs
 Inexpensive modules (<$5,000) can be
produced using wireless and other integrated
circuits
 In addition to new vehicles, existing vehicles
can be retrofitted with these modules
 No need for LIDAR because of constrained
environment
 Lower costs enable faster diffusion
 Faster diffusion enables faster implementation
of roads dedicated to AVs

29. Multiple Scenarios Can be Pursued
Simultaneously
 Scenario emphasized in these slides is design
autonomous roads for AVs
 This can be pursued even as mixed road scenario is
pursued
 High-end AVs are sold and they are used on roads with
manually driven cars
 These AVs will likely require divers for many years
 But if they are successful, the drivers and the driving wheel
may be eliminated, thus promoting the diffusion of these
high-end AVs
 Once these AVs have diffused, cities might pursue fully
autonomous roads

30. Many Challenges for Autonomous Roads
 Need a good architecture and conceptual design
for both system and vehicle modules
 Need cellular infrastructure suppliers to work
with automobile companies, component
suppliers, and cities to design and test systems
 Tests would be required under many types of
weather situations
 The goal should be operational systems by 2025,
just as 5G has begun to diffuse

31. Many Challenges (2)
 Changeover from existing to autonomous roads will
be difficult
 Will enough people be willing to purchase modules
to justify fast changeover?
 Or will autonomous roads be under utilized for many
years, thus wasting scarce resource of land?
 What about people who don’t buy modules?
 If they can’t use specific highway, what can they do?
 They must be given viable alternatives
 Can we offer them public transport or inexpensive
multiple passenger ride sharing services?
 Will they accept change or fight it?

32. Many Challenges (3)
 Alternatively, can we begin with lanes in roads, rather
than entire roads?
 Dedicate one lane to AVs
 This would allow gradual switch from fully manual to
fully autonomous road
 One problem:
 when highways are crowded, only the AV lane will be
moving
 How would an AV exit in this situation?
 Would all the AVs have to stop for an AV to exit?

33. Summary
 AVs are quickly becoming cheaper
 But their costs will remain high and their benefits low
until we have fully autonomous roads
 Developing these roads should be the goal of AVs
 For naysayers, technologies have always been initially
implemented in constrained environments
 AVs should also be implemented in this way in order
 increase safety
 reduce costs of implementation
 increase benefits from implementation