Automated Cars

by
AlAin l. KornhAuser, PhD, F. iTe
Professor, Operations Research & Financial Engineering
Director, Program in Transportation
Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering)
Princeton University
Presented at
ITE MetSection Princeton Meeting
NYC, Javits Center
May 12, 2015
Automated Cars:
from Today to the
Ultimate Riding Machine

FromToTo
The ultimate
riding machine

• What is the Problem/Opportunity?
• Where are we today with Smart Driving Cars?
• What is in it for You?
• How might you get it?
• Discussion & Other Issues
Outline

Outline

Quality of Life 101: Important Elements
• Environment
– Clean air, water, …
• Employment
– “High Quality” Jobs
– Correlated with Crime/Personal Safety
• Mobility
– Safety, Efficiency, Equity, Comfort, Convenience, …
• Main Premise:
– We can and will Create Smart Driving Technology:
• Cost (Technology) < Net Present Value { Expected [ Reduced Liability (Technology)]}
-> Insurance can pay for adoption of the technology AND make more money
– “High Quality” Jobs are created,
– Mobility is enhanced,
– Lives are saved, injuries avoided and disruption averted, …
– Environment is improved
– All for FREE!

The Problem….
We Love the Car’s Freedom & Mobility
But…Continuous Vigilance is an unrealistic requirement for drivers
http://orfe.princeton.edu/~alaink/SmartDrivingCars/Videos/HIGHWAY_SING-A-LONG_%20

Early Estimate of Motor Vehicle Traffic Fatalities in 2012

TravelTainment Industry Wants Everyone’sAttention

Txtng while driving is out of control…

In In 717 out of 723 accidents ((99%)
http://orfe.princeton.edu/~alaink/SmartDrivingCars/NHTSA_Hendricks2001_UnsafeDrivingActs.pdf
“In 717 out of 723 crashes (99%), a driver behavioral
error caused or contributed to the crash”

Present (2010) Economic & Societal Impact of Motor Vehicle
Crashes
http://www.nhtsa.gov/About+NHTSA/Press+Releases/2014/NHTSA-study-shows-vehicle-crashes-have-$871-billion-impact-on-U.S.-economy,-society
imPlicATions ?
oF jusT level 2
Automated Collision Avoidance and Lane Centering
leT’s Follow The money

Crashes

Crashes
Insurance
Charities
Federal Gov.
Local Government
Driver’s Families
Deductible
Federal Gov.
Local Government

Crashes
Charities
Federal Gov.
Local Government
Driver’s Families
Deductible
Federal Gov.
Local Government

Crashes
Charities
Driver’s Families
Deductible
Federal Gov.
Local Government

What am I trying to do?
• Action Item:
– Leverage NJ’s assets @ Fort Monmouth
• Home of major Insurance, auto manufacturers, technology,
communications and education
– Create the Research, Certification & Commercialization
Environment :
To Develop SmartDrivingTechnology (SDT) such that:
PriceSDTSDT < NetPresentValue {
Expected {AccidentLiability w/o SDT }
- Expected {AccidentLiability w SDT }}
This suggests a very favorable business opportunity for Insurers
NJ’s Insurance Companies thus have vested fiduciary and societal
interests in contributing to the achievement of this action item.

After Economic & Societal Impact of Motor Vehicle Crashes

After Economic & Societal Impact of Motor Vehicle Crasheshttp://www.nhtsa.gov/About+NHTSA/Press+Releases/2014/NHTSA-study-shows-vehicle-crashes-have-$871-billion-impact-on-U.S.-economy,-society

Relative Economic & Societal Impact of Motor Vehicle Crashes
AFTER TODAY

DOT HS 810 767 Pre-Crash Scenario Typology for Crash Avoidance Research
More on Google: Levandowski Presentation

Response is Laudable
Kirkland, WA
But… Not Likely to be Effective
April 3: US DOT Launches First-Ever National Distracted Driving Enforcement and Advertising Campaign

At one time we thought….
http://www.youtube.com/watch?v=Rx6keHpeYakhttp://www.youtube.com/watch?v=Rx6keHpeYak

NOT: Future Vehicle Technologies
http://www.youtube.com/watch?v=Rx6keHpeYak

We really Need to get
to
Even though Safety Doesn’t Sell,
Some Automakers are Leading the Way
Even though Safety Doesn’t Sell,
Some Automakers are Leading the Way
Up to today:
Primarily concerned with safety standards
associated with Crash Mitigation
(air bags, seat belts, crash worthiness, …)
Up to today:
Primarily concerned with safety standards
associated with Crash Mitigation
(air bags, seat belts, crash worthiness, …)
http://orfe.princeton.edu/~alaink/SmartDrivingCars/Videos/Subaru%20EyeSight_Commercial60secCrashTest.mp4

More Likely: Future Vehicle Technologies
http://orfe.princeton.edu/~alaink/SmartDrivingCars/Videos/Subaru%20EyeSight_Commercial60secCrashTest.mp4

https://www.youtube.com/watch?v=yARbNYcjPQM

http://www.youtube.com/watch?v=dWj44GjrSs0

Outline

Blind spot Warning
(automatically senses object in blind spot)
Lane departure Warning
(automatically senses sense lane markings)
Forward collision Warning
(automatically senses object ahead, maintains time-to-collision,
warns appropriately)
Backward collision Warning
(automatically senses object behind, maintains time-to-collision,
warns appropriately)
Anti-lock Braking Systems (ABS)
(automatically adjusts brakes)
Electronic Stability Control (ESC)
(automatically adjusts brakes and throttle)
Adaptive Cruise Control
(automatically brakes, and throttles)
Automated Collision Avoidance
(automatically brakes)
Automated Lane Keeping
(automatically steers vehicle to remain in lane)
Glossary of (Often Confused)
Terms
• Warn the Driver • Take over control
•Autonomous Vehicles – “self driving car”. For the most part, each vehicle figures out what to do
using its own vehicle-borne sensors that “see” what is going on around them.
•Connected Vehicles – vehicles find out what others are doing by “talking” to each other (V2V) and
the infrastructure (V2I)
•Advanced Driver Assistance Systems (ADAS) – help the driver the driver avoid crashes using
warnings or taking over automatically:

Preliminary Statement of Policy Concerning Automated Vehicles
Level 0 (No automation)
The human is in complete and sole control of safety-critical functions (brake, throttle, steering) at all times.
Level 1 (Function-specific automation)
The human has complete authority, but cedes limited control of certain functions to the vehicle in certain normal driving or
crash imminent situations. Example: electronic stability control
Level 2 (Combined function automation)
Automation of at least two control functions designed to work in harmony (e.g., adaptive cruise control and lane centering) in
certain driving situations.
Enables hands-off-wheel and foot-off-pedal operation.
Driver still responsible for monitoring and safe operation and expected to be available at all times to resume control of the
vehicle. Example: adaptive cruise control in conjunction with lane centering
Level 3 (Limited self-driving)
Vehicle controls all safety functions under certain traffic and environmental conditions.
Human can cede monitoring authority to vehicle, which must alert driver if conditions require transition to driver control.
Driver expected to be available for occasional control. Example: Google car
Level 4 (Full self-driving automation)
Vehicle controls all safety functions and monitors conditions for the entire trip.
The human provides destination or navigation input but is not expected to be available for control during the trip. Vehicle
may operate while unoccupied. Responsibility for safe operation rests solely on the automated system
SmartDrivingCars&Trucks
What is a SmartDrivingCar?

Level Insurance
Implications
Value
Proposition
(driver/buyer)
Market Force
(who/what)
Societal Implications

Level Insurance
Implications
Value
Proposition
(driver/buyer)
Market Force
(who/what)
0 “55 Chevy” Many Accidents,
High Claims
Freedom
Life Style
Market Dominance
(Madison Avenue)
Death trap

Level Insurance
Implications
Value
Proposition
(driver/buyer)
Market Force
(who/what)
High Claims
Freedom
Life Style
Market Dominance
(Madison Avenue)
Death trap
0+ “Crash Mitigation” (air
bags, seat belts, energy absorbing)
Many Accidents,
High Claims
Disdain Public sector;
Law enforcement
Fewer deaths

Level Insurance
Implications
Value
Proposition
(driver/buyer)
Market Force
(who/what)
High Claims
Freedom
Life Style
Market Dominance
(Madison Avenue)
Death trap
Many Accidents,
High Claims
Law enforcement
Fewer deaths
1 “1st
generation
automated systems” :
(Cruise Control & Anti-lock Brakes)
Slightly reduced
claims
Some Comfort Public sector Slightly fewer accidents

Level Insurance
Implications
Value
Proposition
(driver/buyer)
Market Force
(who/what)
High Claims
Freedom
Life Style
Market Dominance
(Madison Avenue)
Death trap
Many Accidents,
High Claims
Law enforcement
Fewer deaths
1 “1st
generation
Slightly reduced
claims
2 Attentive Automated
Driving: (Collision Avoidance & Lane
Centering)
50% less
$ liability;
++ profits
More Comfort Will need help from “Flo
& the Gecko” (Insurance
incentivizes adoption)
“50%” fewer accidents;
less severity->

Level Insurance
Implications
Value
Proposition
(driver/buyer)
Market Force
(who/what)
High Claims
Freedom
Life Style
Market Dominance
(Madison Avenue)
Death trap
Many Accidents,
High Claims
Law enforcement
Fewer deaths
1 “1st
generation
Slightly reduced
claims
Centering)
50% less
$ liability;
++ profits
less severity->
3 Un-Attentive Automated
Driving : “Texting Machine”
(Collision Avoidance, Lane Changing &
Centering, Intersection Control)
Even less
$ liability
(~Product liability)
++ profits
Liberation (some
of the
time/places) ;
more Safety
Consumers Pull,
TravelTainment Industry
Push
++ Car sales,
-- insurance claims,
+ VMT

Level Insurance
Implications
Value
Proposition
(driver/buyer)
Market Force
(who/what)
High Claims
Freedom
Life Style
Market Dominance
(Madison Avenue)
Death trap
Many Accidents,
High Claims
Law enforcement
Fewer deaths
1 “1st
generation
Slightly reduced
claims
Centering)
50% less
$ liability;
++ profits
less severity->
3 Un-Attentive Automated
Driving : “Texting Machine”
(Collision Avoidance, Lane Changing &
Centering, Intersection Control)
Even less
$ liability
++ profits
Liberation (some
of the
time/places) ;
more Safety
Consumers Pull,
TravelTainment Industry
Push
++ Car sales,
-- insurance claims,
+ VMT
4 Driverless: “aTaxi “
Able to
Fleet coverage;
Chauffeured,
Mobility Bought
“by the Drink”
rather than “by
the Bottle”
Profitable Business
Opportunity for
Utilities/Transit
Companies
Personal Car = “Bling” not
instrument of personal mobility,
Comm. Design ++;
PMT ?; VMT - -, Energy - -,
Congestion - -, Environment ++

Scope of “Safe Driving Vehicles”
Rivium 2006 ->
Aichi, Japan, 2005 Expo
Automated Guided Vehicles
Tampa Airport 1st
APM 1971
Rio Tinto Automated Truck
Tesla Car Transporter
CityMobil2
Copenhagen Metro
Heathrow PodCar
Milton Keynes, UK
Elevator Mercedes Intelligent Drive
Rio Tinto Automated Train
Volvo Truck

What the Evolving Levels Deliver:What the Evolving Levels Deliver:
Levels 1 -> 2: (Driverless
Repositioning) Safety,
Comfort & Convenience
Levels 1 -> 2: (Driverless
Repositioning) Safety,
Comfort & Convenience
Level 4 (Driverless
Repositioning) : Pleasure,
Mobility, Efficiency,
Equity
Elimination of cost of
Labor Revolutionizes
“Mass Transit” by
Enabling Low-cost to
even single riders
“zero”
An Insurance Discount Play
A Corporate Utility/Fleet Play
Levels 3:
Pleasure, Safety, Comfort
& Convenience
Levels 3:
Pleasure, Safety, Comfort
& Convenience
An Enormous Consumer Play

Where Are We Today?
52
• Level 2.x available today
• 4 states have passed
autonomous vehicle
legislation
• OEMs are working toward
Level 3
• Google is working toward
Level 4
What is the Low
Hanging Fruit?
Buses & Transit!!

Outline

• What abut Buses and Transit?
Outline

ExclusivityoftheWay
Speed
Slow
m
oderate
high
Exclusive
Mixed
Restricted
Scope of Vehicle Automation
NHTSA Levels of Automation
0 1 2 3 4

NHTSA Levels of Automation
0 1 2 3 4
ExclusivityoftheWay
Speed
Slow
m
oderate
high
Exclusive
Mixed
Restricted
Higher Dimensional Scope of Vehicle Automation aTaxi

Terrible News! Claims are going through the
roof!

2011 Nationwide
Bus Casualty and Liability Expense
Source FTA NTD
Casualty and
Liability
Amount
Vehicle-
related
$483,076,010.
Total Buses 59,871
Sub-Total Casualty and
Liability Amount Per Bus
$8,069/Bus/Year

Key Business Model
Cost of Active Collision Avoidance
<
Present Value {Expected Liability Savings over life of bus}

The Cost of Retrofitting an
Active Collision Avoidance System
on a Bus Could be Recovered
in as Little as One Year
Through Reductions in
Casualty and Liability Claims*
* Similarly if included in the Specs for new buses

Liability Savings pay Cash for the Technology, and…
“half” of the following come for FREE!

Think About…Going beyond retrofitting existing buses…
+

+ +
Enormous Extended Reach

+
Inexpensive Guideway + Inexpensive vehicles
Great way to get startedGreat way to get started

Think About…
+ +
Enormous Extended Reach

– Where do you typically travel?
Outline

• Land-Use hasn’t changed
– Trip ends don’t change!
• Assume Trip Distribution Doesn’t Change
– Then it is only Mode Split.
– Do I:
• Walk?
• Ride alone?
• Ride with someone?
• All about Ride-sharing
What about Level 4 Implications on
Energy, Congestion, Environment?
Assuming Planners Don’t Change

Daily Person Trips in New Jersey:
A Synthesis

Most every day…
• Almost 9 Million NJ residents
• 0.25 Million of out of state commuters
• Make 30+ Million trips
• Throughout the 8,700 sq miles of NJ
• Where/when do they start?
• Where do they go?
• Does anyone know???
– I certainly don’t
• Not to sufficient precision for credible analysis

• I’ve harvested one of the largest troves of GPS
tracks
– Literally billions of individual trips,
– Unfortunately, they are spread throughout the western
world, throughout the last decade.
– Consequently, I have only a very small ad hoc sample of
what happens in NJ on a typical day.
I’ve Tried…

Why do I want to know every trip?
• Academic Curiosity
• If offered an alternative, which ones would likely
“buy it” and what are the implications.
• More specifically:
– If an alternative transport system were available,
which trips would be diverted to it and what
operational requirements would those trip impose on
the new system?
• In the end…
– a transport system serves individual decision makers.
It’s patronage is an ensemble of individuals,
– I would prefer analyzing each individual trip
patronage opportunity.

Synthesize from publically available data:
•“every” NJ Traveler on a typical day NJ_Resident file
– Containing appropriate demographic and spatial
characteristics that reflect trip making
•“every” trip that each Traveler is likely to make
on a typical day. NJ_PersonTrip file
– Containing appropriate spatial and temporal
characteristics for each trip

Creating the NJ_Resident file
for “every” NJ Traveler on a typical day
NJ_Resident file
Start with Publically available data:

2010 Population census @Block Level
– 8,791,894 individuals distributed 118,654 Blocks.
County Population Census Blocks Median Pop/ Block Average Pop/Block
ATL 274,549 5,941 26 46
BER 905,116 11,171 58 81
BUR 448,734 7,097 41 63
CAM 513,657 7,707 47 67
CAP 97,265 3,610 15 27
CUM 156,898 2,733 34 57
ESS 783,969 6,820 77 115
GLO 288,288 4,567 40 63
HUD 634,266 3,031 176 209
HUN 128,349 2,277 31 56
MER 366,513 4,611 51 79
MID 809,858 9,845 50 82
MON 630,380 10,067 39 63
MOR 492,276 6,543 45 75
OCE 576,567 10,457 31 55
PAS 501,226 4,966 65 101
SAL 66,083 1,665 26 40
SOM 323,444 3,836 51 84
SUS 149,265 2,998 28 50
UNI 536,499 6,139 61 87
WAR 108,692 2,573 23 42
Total 8,791,894 118,654 74.1

Assigning a Daily Activity (Trip) Tour to Each PersonAssigning a Daily Activity (Trip) Tour to Each Person

Final NJ_Resident fileFinal NJ_Resident file
Home County
Person Index
Household Index
Full Name
Age
Gender
Worker Type Index
Worker Type String
Home lat, lon
Work or School lat,lon
Work County
Work or School Index
NAICS code
Work or School start/end time
ATL 274,549
BER 905,116
BUR 448,734
CAM 513,657
CAP 97,265
CUM 156,898
ESS 783,969
GLO 288,288
HUD 634,266
HUN 128,349
MER 366,513
MID 809,858
MON 630,380
MOR 492,276
OCE 576,567
PAS 501,226
SAL 66,083
SOM 323,444
SUS 149,265
UNI 536,499
WAR 108,692
NYC 86,418
PHL 18,586
BUC 99,865
SOU 13,772
NOR 5,046
WES 6,531
ROC 32,737
Total: 9,054,849

Creating the NJ_PersonTrip file
• “every” trip that each Traveler is likely to make on a
typical day. NJ_PersonTrip file
– Containing appropriate spatial and temporal
characteristics for each trip
• Start with
– NJ_ResidentTrip file
– NJ_Employment file
• Readily assign trips between Home and Work/School
– Trip Activity -> Stop Sequence
• Home, Work, School characteristics synthesized in NJ_Resident file

Assigning “Other”
Locations
Attractiveness (i)= (Patrons (I)/AllPatrons)/{D(i,j)2
+ D(j,k)2
};
Where i is destination county; j is current county; k is home county
1. Select Other County
Using:
Attractiveness-Weighted
Random Draw
2. Select “Other” Business using:
Patronage-Weighted Random Draw within selected county

Assigning Trip
Departure Times
• For: H->W; H->School; W->Other
• Work backwards from Desired Arrival Time using
• Distance and normally distributed Speed distribution, and
• Non-symmetric early late probabilities
• Else, Use Stop Duration with non-symmetric early late probabilities
based on SIC Cod
Distribution of
Arrival/Departure
Times
Trip Type; SIC
Time Generator:
RandomDraw:
Time Distribution
Trip Departure time
(SeconsFromMidnight)
Task 8

NJ_PersonTrip file
• 9,054,849 records
– One for each person in NJ_Resident
file
• Specifying 32,862,668 Daily
Person Trips
– Each characterized by a precise
• {oLat, oLon, oTime, dLat, dLon, Est_dTime}
All Trips
Home
County
Trips TripMiles AverageTM
# Miles Miles
ATL 936,585 27,723,931 29.6
BER 3,075,434 40,006,145 13.0
BUC 250,006 9,725,080 38.9
BUR 1,525,713 37,274,682 24.4
CAM 1,746,906 27,523,679 15.8
CAP 333,690 11,026,874 33.0
CUM 532,897 18,766,986 35.2
ESS 2,663,517 29,307,439 11.0
GLO 980,302 23,790,798 24.3
HUD 2,153,677 18,580,585 8.6
HUN 437,598 13,044,440 29.8
MER 1,248,183 22,410,297 18.0
MID 2,753,142 47,579,551 17.3
MON 2,144,477 50,862,651 23.7
MOR 1,677,161 33,746,360 20.1
NOR 12,534 900,434 71.8
NYC 215,915 4,131,764 19.1
OCE 1,964,014 63,174,466 32.2
PAS 1,704,184 22,641,201 13.3
PHL 46,468 1,367,405 29.4
ROC 81,740 2,163,311 26.5
SAL 225,725 8,239,593 36.5
SOM 1,099,927 21,799,647 19.8
SOU 34,493 2,468,016 71.6
SUS 508,674 16,572,792 32.6
UNI 1,824,093 21,860,031 12.0
WAR 371,169 13,012,489 35.1
WES 16,304 477,950 29.3
Total 32,862,668 590,178,597 19.3

New Jersey Summary Data
Item Value
Area (mi2
) 8,061
# of Pixels Generating at Least One O_Trip 21,643
Area of Pixels (mi2
) 5,411
% of Open Space 32.9%
# of Pixels Generating 95% of O_Trips 9,519
# of Pixels Generating 50% of O_Trips 1,310
# of Intra-Pixel Trips 447,102
# of O_Walk Trips 1,943,803
# of All O_Trips 32,862,668
Avg. All O_TripLength (miles) 19.6
# of O_aTaxi Trips 30,471,763
Avg. O_aTaxiTripLength (miles) 20.7
Median O_aTaxiTripLength (miles) 12.5
95% O_aTaxiTripLength (miles) 38.0

• How might you get it?
Outline

• By walking to a station/aTaxiStand
– At what point does a walk distance makes the
aTaxi trip unattractive relative to one’s personal
car?
– ¼ mile ( 5 minute) max
• Like using an Elevator!
Sharing aTaxis
Elevator

Elevator Analogy of an aTaxi Stand
Temporal Aggregation
Departure Delay: DD = 300 Seconds
Kornhauser
Obrien
Johnson
40 sec
Henderson
Lin
1:34
Popkin
3:47

Samuels
4:50
Henderson
Lin
Young
0:34
Popkin
2:17
Elevator Analogy of an aTaxi Stand
60 seconds later
Christie
Maddow
4:12

Typical Daily NJ-wide AVO
CD: Common Destinations; DD: Departure Delay (in Seconds)
Typical Daily NJ-wide AVO
CD: Common Destinations; DD: Departure Delay (in Seconds)

Public Schools in the USPublic Schools in the US

Nation-Wide BusinessesNation-Wide Businesses
Rank State
Sales
Volume No. Businesses
1 California $1,889 1,579,342
2 Texas $2,115 999,331
3 Florida $1,702 895,586
4 New York $1,822 837,773
5 Pennsylvania $2,134 550,678
9 New Jersey $1,919 428,596
45 Washington DC $1,317 49,488
47 Rhode Island $1,814 46,503
48 North Dakota $1,978 44,518
49 Delaware $2,108 41,296
50 Vermont $1,554 39,230
51 Wyoming $1,679 35,881
13.6 Million Businesses
{Name, address, Sales, #employees}
13.6 Million Businesses
{Name, address, Sales, #employees}

US_PersonTrip file will have..
• 308,745,538 records
– One for each person in US_Resident file
• Specifying 1,009,332,835 Daily Person
Trips
– Each characterized by a precise
• {oLat, oLon, oTime, dLat, dLon, Est_dTime}
• Will Perform Nationwide aTaxi AVO analysis
• Results

Manhattan (New York County)
• Simulated population of 1,585,873 residents
• 8,085,055 trips originate within Manhattan
• 52,759,156 person-trip miles for Manhattan
oTrips
• 3,010,666 unique travelers (1,424,793 non-
resident travelers – Commuters)
• Mean Trip Length = 6.53 miles; Median Trip
Length = 3.31 miles
• Interesting differences between commuter and
resident population traveling through Manhattan

2013 NY TLC Stats
• 168,779,842 TLC Trips during 2013 calendar year
– 66% 35 or under; 35% 21<->35
• 42,821 drivers and 13,741 unique taxicabs.
• 1,155,367 unique taxicab pickups @ Penn Station

Concentration of departure “areas

Take-away…
• Except for from a few locations, the spatial and
temporal distribution of 2013 taxi trips is VERY
DIFFUSE!
• In order to achieve a 25% reduction in occupied taxi
miles, service would have to substantially degrade
making users wait up to 5 minutes before departure
and traveling as much as 20% father/longer to drop
off ride sharers in as many as 2 intermediate
locations.
• Opening up ride-sharing to all other trips would
certainly increase ridesharing without increasing
VMT; HOWEVER, these trips would be take away
from walking or reduce existing ride-sharing on
conventional buses an subways.

• Discussion & Other Issues
Outline

Action Item:
– Leverage NJ’s assets @ Fort Monmouth
• Home of major Insurance, auto manufacturers, technology,
communications and education
– Create the Research, Certification & Commercialization
Environment:
To Develop SmartDrivingTechnology (SDT) such that:
PriceSDTSDT < NetPresentValue {
Expected {AccidentLiability w/oSDT}
- Expected {AccidentLiability wSDT}}
• The environment has the potential to be more “open” than
similar private facilities recently announced.
What are Companies Trying To Do?What are Companies Trying To Do?

The Initial Project:The Initial Project:
Team:
(with Roscoe, Soterea, American Public Transit Association (APTA), NJ Transit, NY
MTA, Munich Re and insurance pools from WA, CA, OH & VA)
Focused on
Research, Certification and Commercialization
of
SmartDriving Technology for Buses
We are currently seeking funds to advance this projectWe are currently seeking funds to advance this project

• Background on SmartDrivingCars
• Effort to Establish “Center for Research, Certification &
Commercialization of Automated Road Vehicles @ Fort
Monmouth”
Outline
(what I’m involved with )

NASA, Google announce lease at Ames Research Center AmesNASA, Google announce lease at Ames Research Center Ames
http://www.nasa.gov/centers/ames/pdf/255793main_June.08.Agram.smallfile.pdf

Mercedes-Benz sends autonomous automobiles onto the USA's most
extensive testing ground
Mercedes-Benz sends autonomous automobiles onto the USA's most
extensive testing ground
http://www.cnbc.com/id/102049263

Why Fort
Monmouth?
• Observation:
– Ft Monmouth may well be one of the most hospitable
sites for those with a Vested Interest in
SmartDrivingTechnology to advance their intentions.
• Action Item:
– Create a Testing & Certification Environment for use
by Those with a Vested Interest to collaboratively
transition their products from Research to Full
Commercialization

Vehicle Bays
McAfee Office Building
~ 100,000sf
Support Buildings
Center for Research of SmartDrivingCars (CRS)
at Fort Monmouth
McAfee Complex
1250’ x 865’ ~ 25.8 acres
at Fort Monmouth
McAfee Complex
1250’ x 865’ ~ 25.8 acres

at Fort Monmouth
Exclusive Use Roadways Area
~ 60 acres
at Fort Monmouth
Exclusive Use Roadways Area
~ 60 acres
Vehicle Bays
McAfee Office Building
~ 100,000sf
Support Buildings

at Fort Monmouth
Mixed Use Roadways
½ The Fort ~ 1 sq. mile
at Fort Monmouth
Mixed Use Roadways
½ The Fort ~ 1 sq. mile
Exclusive Use
Roadways

The “Visionaries”
FMERA:
Vision for World-Class Advanced Technology Center @ The Fort
Princeton University:
Facilitating FMERA’s Vision with a World-Class
Center for Research, Certification and Commercialization of SmartDrivingCars (&Trucks & Buses) (RCCS) (pronounced “R Sis”)
The Team
The “Players” to be “Drafted” (Green: Substantive Discussions; Red: Initial Discussions; Black: Yet to be approached)
The “Venue”
The McAfee Center and The Fort’s Roadways
The “Coaching Staff”
CARTS
Corporation for Autonomous Roadway & Transit Systems
Automakers Suppliers Facilitators Insurance Universities Public Sector
Ford Siemens Bertram Capital Munich Re Princeton G Cleve Transit
Subaru (HQ-NJ) autonomouStuff DCH AutoGroup NJ Manufacturers Monmouth NJ DMV
BMW (HQ-NJ) MobilEye VisLab Ins. Inst. for Hwy Safety CCNY Reg. 2 URC Monmouth County
Mercedes Benz (HQ-NJ) Texas Inst Verizon State Farm U of Maryland NJ Transit
Volvo Bosch Qualcomm Progressive NJIT FHWA+FTA
Jaguar Continental AT&T Geico Rutgers NJ DoT

ModalDimension
Sector Dimension
CommercializationCertificationResearch
TransitCarsTrucks
Center’s Activity Matrix

• Initial Focus is China
• Expect to have 1st
Product Launch by July 2015
• Initial Focus on Trucks,
• Then Buses,
• Finally Cars.

Federal Transit Administration
National Transit Database for 2013
• Commuter Bus (CB), Motor Bus (MB), Bus Rapid
Transit (RB), Demand Responsive (DR)
• 119 Fatalities
• 15,351 Injuries
• Casualty & Liability expenses paid =
• $499,872,628
• Average of $6,187 per bus

In the next five days the bus
transit industry will spend $6.8
million in casualty and liability
expenses

Casualty and Liability Claims are a
Huge Drain on the Industry
• For the 10 year period 2004-2013, more than
$4.8 Billion was spent on casualty and
liability claims
• For many self-insured transit agencies these
expenses are direct “out-of-pocket”
• Large reserves for claims must be budgeted
• Claims experience is reflected in insurance
premiums
• There are gaps in data reporting

Costs of Bus Crashes – Industry Wide
Tangible – reported as casualty and liability expense
•Physical damage insurance premiums
•Recovery of physical damage losses for public liability and
property damage insurance premiums
•Insured and uninsured public liability and property damage
settlement pay outs and recoveries
•Other corporate insurance premiums (e.g., fidelity bonds,
business records insurance)

Thank You
alaink@princeton.edu
www.SmartDrivingCar.com
Discussion!

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