Automated Mobility(a.k.a. Self-Driving Cars – and More)

Dr. David L. Boddebodded@alum.mit.edu

Leavenworth Lions Club29 December, 2016

Three Takeaways1. Self driving cars are not enough• Part of integrated, optimized system• Vehicle sharing•Mass transit• Traffic management

2. Pathway to self-driving will be incremental3. Artificial intelligence: the key technology• Disrupts much of the current world

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

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.

Consequences: Challenge Sustainability of the City

• Traffic Congestion: wasted time, money• Urban pollution: environmental quality, health issues

Safety Also a Benefit, but…

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

Automated Driving – Hardware

• LIDAR - Light detection and ranging

• Ultrasound• Radars• Cameras• Sensors – 750 MB/s• Onboard computers –

20 decisions/s

Current Challenge: InterpretationFind the Traffic Light

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.

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?

Takeaway:Self-driving Will Proceed Incrementally

• Self driving cars will have controls• Problem of distraction•Without controls, insurance transfers liability to manufacturer

Artificial intelligence: the Key Technology

• Rate of change is exponential• The better it gets, the better it can get

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

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

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

Deep LearningResolving 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

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

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

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

LyftSystem Integrator

AV Pool

Vehicle Owners(fleet) Hertz

Vehicle Owners(individual)

General MotorsVehicle Designer

Google?

IPO InvestmentsTencent HoldingsAlibaba

VC InvestmentsFontinalis PartnerSierraMaya 360Coatue ManagementIcahn Enterprise

Early InvestmentsAdjacent Competitors

Didi KuaidiGrab Taxi

Ola

Service Suppliers

Waze (Google)Route Planning

StripeTransaction payments

Vehicle Design Assembler

Industrial Alliance

Infrastructure ProviderShell Gas Station

Lyft as Emergent Entrepreneurial Network

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

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

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