Dad's presentation to the Leavenworth, KS Lions Club.

1. Automated Mobility
(a.k.a. Self-Driving Cars – and More)
Dr. David L. Bodde
bodded@alum.mit.edu
Leavenworth Lions Club
29 December, 2016

2. Three Takeaways
1. Self driving cars are not enough
• Part of integrated, optimized system
• Vehicle sharing
• Mass transit
• Traffic management
2. Pathway to self-driving will be incremental
3. Artificial intelligence: the key technology
• Disrupts much of the current world

3. If Self-Driving Cars are the Solution…
What Was the Problem?

4. The Urban Mobility Challenge:
Accommodate Growing Populations
• World population concentrating in urban areas
• Mega cities: Tokyo, Mexico City, Beijing
• Next-tier cities: Kansas City, Florence
• As of 2015, 37
megacities.
• 50% of world
population
lives in cities
today
• Increase to 67
percent by
2050.

5. Consequences:
Challenge Sustainability of the City
• Traffic Congestion: wasted time, money
• Urban pollution: environmental quality, health issues

6. Safety Also a Benefit, but…

7. Takeaway:
Beginning of Wisdom = Think Outside the Car
• Self-driving cars by themselves don’t do the job
• Mobility system must be optimized
• Road Infrastructure
• Ride sharing
• Traffic management
• Mass transit: bus and rail

8. Automated Driving – Hardware
• LIDAR - Light detection
and ranging
• Ultrasound
• Radars
• Cameras
• Sensors – 750 MB/s
• Onboard computers –
20 decisions/s

9. Current Challenge: Interpretation
Find the Traffic Light

10. SAE Automated Vehicle Classifications
• Level 0: Automated system has no vehicle control, but may issue warnings.
• Level 1: Driver ready to take control at any time. Automated system may include features
such as Adaptive Cruise Control (ACC), Parking Assistance with automated steering, and
Lane Keeping Assistance (LKA) Type II in any combination.
• Level 2: Driver is obliged to detect objects and events and respond if the automated
system fails to respond properly. The automated system executes accelerating, braking,
and steering. The automated system deactivates immediately upon takeover by driver.
• Level 3: Within known, limited environments (such as freeways), drivers can safely turn
their attention away from driving tasks.
• Level 4: Automated system can control the vehicle in all but a few environments such as
severe weather. The driver enables automated system only when safe to do so. When
enabled, driver attention not required.
• Level 5: Human sets destination, starts automated system. System can drive anywhere it
is legal to drive.

11. Two Potholes:
the Road to Self-driving Cars
• Road-sharing in transition…
1. Ambiguity: how will ambiguous traffic situations be
resolved?
2. Emergency: how will the automated system handle
unanticipated emergencies?
• By itself?
• Default to driver?
• Default to remote controller?

12. Takeaway:
Self-driving Will Proceed Incrementally
• Self driving cars will have controls
• Problem of distraction
• Without controls, insurance transfers liability to manufacturer

13. Artificial intelligence: the Key Technology
• Rate of change is exponential
• The better it gets, the better it can get

14. Comparisons – Computer v. Human Brain
• Human brain capacity (approximate)…
• 100 x 109 neurons times
• 103 connections per neuron times
• 200 “calculations” per second per connection =
• 20 x 1015 calculations per second (cps)
• IBM “Blue Gene” supercomputer
• 1 x 1015 cps by 2005

15. Implications - Computers
• Apply simple curve fitting model, and…
• Computers achieve one human brain capacity (2x1016 cps)
for $1000 by 2023…
• One human brain capacity for $.01 by 2037
• One human race capacity for $1000 by 2049
• One human race capacity for $.01 by 2059
• Necessary…but insufficient to provide the services
required of computation
• More required than raw computing power

16. If computers do only what we tell them to do, they will never
solve the Ambiguity Problem or the Emergency Handoff Problem

17. Deep Learning
Resolving Ambiguity
• How to “train” a DL algorithm?
• Consider experience with GO
• Complex game – 19 x 19 matrix
• 10170 possible board position combinations
• Limited ability to assess sequential consequences of moves
• Overwhelming number of scenarios
• No point counting as in chess (8 x 8 matrix)
• No algorithm as in Blackjack or Roulette
• Computer must use strategy against opponent
• May 2016 Google Deep Mind computer, AlphaGO, beat world GO
champion

18. Teaching Computers to Compete
• Piloting an AV through Holland Tunnel similar – competitive too
• To succeed, AV computer must “think” and act like human
• Learn assertiveness
• Learn limits to assertiveness
• And what are the implications if computers come to think like humans?
• We can think evil thoughts…will they?
• How will we teach computers morality when we practice it so poorly ourselves?
"Go is a game primarily about intuition and feel,
rather than brute calculation, which is what
makes it so hard for computers to play well.“
Demis Hassabis, CEO of Google DeepMind

19. Consequences of Deep Learning:
Winner-Take-All Market
• Deep Learning leads to winner-take-all market
• Early information lead means DL system forms better hypotheses
• Better hypotheses make more efficient use of experience
• More experience builds the lead faster
• Establishes a virtuous cycle that is always ahead
• The AV system competitor who gains early advantage in solving the
ambiguity and handoff problems can take entire market – the hare
wins
• Competitive race turns on information
• Business models that allow more effective information gathering
preferred

20. Consequences of DL for All Competitors
• Networked Innovation models emerge to displace traditional R&D
• Single firm lacks scope to follow all promising pathways
• Classic competitive market (many small competitors responding to price
signals) fails to deliver services requiring system-wide knowledge
• Network model advantages
• Speed
• Flexibility
• Rapid response to opportunity
• Network economies of scope
• Network economies of scale

21. Lyft
System Integrator
AV Pool
Vehicle Owners
(fleet)
Hertz
Vehicle Owners
(individual)
General Motors
Vehicle Designer
Google?
IPO Investments
Tencent Holdings
Alibaba
VC Investments
Fontinalis Partner
SierraMaya 360
Coatue
Management
Icahn Enterprise
Early Investments
Adjacent Competitors
Didi Kuaidi
Grab Taxi
Ola
Service Suppliers
Waze (Google)
Route Planning
Stripe
Transaction
payments
Vehicle Design
Assembler
Industrial Alliance
Infrastructure
Provider
Shell Gas Station
Lyft as Emergent
Entrepreneurial Network

22. Consequences for Traditional Auto Industry
• Increasingly mobility delivered as a
service, not as owned object
• Duty cycle of average LDV: 10-12%
• Problem is fixed cost of ownership
spread over few miles
• Most true in urban areas
• Simulation studies show equivalent
urban service with 20-30% of
vehicles
• Within 10 years, traditional auto
makers in steep decline

23. Takeaways
•AI the Key technology to watch
•DL creates winner-take-all market
•Compels new networked models for R&D
•Mobility as a service – fewer cars needed

24. For later questions or copy of slides:
bodded@alum.mit.edu