the darwinian evolution of smartdrivingcar s

by

Alain L. Kornhauser, PhDProfessor, Operations Research & Financial Engineering

Director, Program in Transportation Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering)

Princeton University

Presented at

PAVE – Summer WorkshopPrinceton, NJAugust 4-6, 2014

The Darwinian Evolution of SmartDrivingCars

AHS: Automated Highway Systems: 1939 -

“Waterloo” may well be the word “System”

1997

APM: Automated People Mover: 1968 -

“Waterloo” limited to serve “Few to Few” demand

PRT: Personal Rapid Transit: 1968 -

Attempt to serve “Many to Many” but“Waterloo” may well be the word “Personal” & Exclusive Guideway?

V2V: Connected Vehicles: 1997 -

“Waterloo” may well be: Zero value until market penetration is high

SDC: SmartDrivingCars: 2004 -

“Waterloo” may well be: Government & Bureaucracy

Real beauty is in its “autonomy”: Benefits are derived by each equipped vehicle all by itself”

CityMobil2

Preliminary Statement of Policy Concerning Driverless CarsLevel 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 centeringLevel 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 carLevel 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

Smar

tDriv

ingC

ars

& T

ruck

s

What is a SmartDrivingCar?


Level “Less” Value Proposition Market Force Societal Implications

Preliminary Statement of Policy Concerning Driverless Cars



0 “55 Chevy” Zero Zero Zero Zero





1 “Cruise Control”

Infinitesimal Some Comfort Infinitesimal Infinitesimal







2 “Collision Avoidance & Lane Centering”

Infinitesimal Much Safety(but Consumers don’t

pay for Safety)

Needs help From “Flo & the Gecko” (Insurance incentivizes adoption)

“50%” fewer accidents; less severity-> 50% less

insurance $ liability









pay for Safety)




3 “Texting Machine”

Some Liberation (some of the time/places) ; more

Safety

Consumers Pull, TravelTainment Industry

Push

Increased car sales, many fewer insurance claims, slight + in VMT









pay for Safety)




3 “Texting Machine”

Some Liberation (some of the time/places) ; more

Safety

Consumers Pull, TravelTainment Industry

Push

Increased car sales, many fewer insurance claims, slight + in VMT

4 “aTaxi “ Always Chauffeured, Buy Mobility “by the

Drink” rather than “by the Bottle”

Profitable Business Opportunity for

Utilities/Transit Companies

Personal Car becomes “Bling” not instrument of personal mobility,

VMT ?; Comm. Design ? Energy, Congestion,

Environment?


What the Levels Deliver:What the Levels Deliver:

Levels 1 -> 2: Increased Safety, Comfort & ConvenienceLevels 1 -> 2: Increased Safety, Comfort & Convenience

Level 4 (Driverless Repositioning) : Pleasure, Mobility, Efficiency, Equity Revolutionizes “Mass Transit” by Greatly Extending the Trips that

can be served @ “zero” cost of Labor.(That was always the biggest “value” of PRT; zero labor cost for even zero-occupant trips)

Primarily an Insurance Discount Play

A Corporate Utility/Fleet Play

Levels 3: Increased Pleasure, Safety, Comfort & ConvenienceLevels 3: Increased Pleasure, Safety, Comfort & Convenience

An Enormous Consumer Play


Hmmm... this is enormously tragic because existing collision avoidance technology could have likely avoided this accident altogether even if Mr. Roper had not slept for 48 hours or was in complete compliance with all "hours of service regulations". Even if Mr. Roper had not slept for 24 hours, tougher hours of service regulations would not have prevented this accident. What would have prevented this accident would have been the availability of collision avoidance technology on this truck. If Walmart somehow feels indisposed by this accident and wants to react constructively, Walmart should contribute to the advancement of collision avoidance technology and insist that all trucks moving their goods be equipped with such technology! In fact, calling this an accident may well be a misnomer; maybe we should call it irresponsibility on Walmart’s part for not insisting that the trucks serving their stores have this technology. The cost of this technology may well evolve to be more than offset by the reduction in truck insurance expense. In other words, Walmart would not be indisposed and save money. That doesn’t sound like an accident to me. It sounds like fiduciary (and societal) irresponsibility on the part of Walmart.

Of course, Walmart is not the only business that relies on long haul truckers to supply goods to its stores. The Tracy Morgan collision should be a wake up call for businesses that rely on large trucks on US roads every day driven by drivers operating under pressure on deadlines. Now that collision avoidance technology is available, Walmart and other business should insist that their logistics partners use trucks equipped with this technology. They will save money in the long run and lives in the short and long runs. Alain

Thank [email protected]

www.SmartDrivingCar.com

Discussion!

What About Buses?

Use Autonomous Collision Avoidance Technology to Address a

BIG CURRENT Transit Problem

Good News! Travel by Bus is getting safer!

Good News! Injuries have been trending down!

Terrible News! Claims are going through the roof!

Casualty and Liability Claims are a Huge Drain on the Industry

• For the 10 year period 2002-2011, more than $4.1 Billion was spent on casualty and liability claims

• For many self-insured transit agencies these expenses are direct “out-of-pocket”

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

The Cost of Installing an Active Collision Avoidance System

on a Bus Could be Recovered in as Little as One Year Through Reductions in

Casualty and Liability Claims

Why New Jersey?

• Observation: In 2 Years, NJ Transit will initiate a new Bus Replacement Cycle (That will extend for about 15 years)

• Action Item:– Ensure that the Procurement Specifications include

“Level 2” SmartDriving Technologies

Near-term Opportunity for a Substantive Extension of Autonomous Transit

• Specific: General Mobility for Fort Monmouth Redevelopment– Currently: Decommissioned Ft. Monmouth is vacant .

• Ft. Monmouth Economic Revitalization Authority (FMERA) is redeveloping the 3 sq. mile “city”• Focus is on attracting high-tech industry• The “Fort” needs a mobility system.• FMERA is receptive to incorporating an innovative mobility system• Because it is being redeveloped as a “new town” it can accommodate itself to be an ideal site for testing

more advanced driverless systems.

The Initial Project:The Initial Project:

Princeton University (with American Public Transit Association (APTA), Greater Cleveland Transit, and

insurance pools from WA, CA, OH & VA)

Focused on

Research, Certification and Commercializationof

SmartDriving Technology to Buses

Pending $5M Grant from Federal Transit Administration

Proposal Done: December 2, 2013:For next 6 months: Silence from FTAProposal Done: December 2, 2013:

For next 6 months: Silence from FTA

In those 6 months approximately:39 Fatalities

7,200 Injuries$180M Claims

“Level 2 Collision Avoidance Technology” Could cut those numbers in half

Why the delay in spending $5M to get the process started ???????



Discussion!

• Assuming PLANNERS continue to PLAN as they do now. – How will people “get around”?

• Assuming this new way of “getting around” offers different opportunities and constraints for PLANNERS to improve “Quality of Life”. – How will Zoning/Land-Use Change?– How will people “get around”?

What about Level 4 Implications on Energy, Congestion, Environment?

• 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

• “AVO < 1” RideSharing– “Daddy, take me to school.” (Lots today)

• “Organized” RideSharing– Corporate commuter carpools (Very few today)

• “Tag-along” RideSharing– One person decides: “I’m going to the store.

Wanna come along”. Other: “Sure”. (Lots today)• There exists a personal correlation between ride-sharers

• “Casual” RideSharing– Chance meeting of a strange that wants to go in

my direction at the time I want to go • “Slug”, “Hitch hiker”

Kinds of RideSharing

• “AVO < 1” RideSharing– Eliminate the “Empty Back-haul”; AVO Plus

• “Organized” RideSharing– Diverted to aTaxis

• “Tag-along” RideSharing– Only Primary trip maker modeled, “Tag-alongs”

are assumed same after as before.

• “Casual” RideSharing– This is the opportunity of aTaxis– How much spatial and temporal aggregation is

required to create significant casual ride-sharing opportunities.

aTaxis and RideSharing

• 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!

Spatial Aggregation

Elevator

• No Change in Today’s Walking, Bicycling and Rail trips

– Today’s Automobile trips become aTaxi or aTaxi+Rail trips with hopefully LOTS of Ride-sharing opportunities

What about Level 4 Implications on Energy, Congestion, Environment?Assuming Planners Don’t Change

Pixelation of New Jersey

NJ State GridZoomed-In Grid of Mercer

Pixelating the State with half-mile Pixels

xPixel = floor{108.907 * (longitude + 75.6)}yPixel = floor{138.2 * (latitude – 38.9))xPixel = floor{108.907 * (longitude + 75.6)}yPixel = floor{138.2 * (latitude – 38.9))

a PersonTrip {oLat, oLon, oTime (Hr:Min:Sec) ,dLat, dLon, Exected: dTime}

O

O

DP1

An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec) , }

An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec) ,dYpixel, dXpixel, Exected: dTime}

P1

O

Common Destination (CD)CD=1p: Pixel -> Pixel (p->p) Ride-sharing

TripMiles = LTripMiles = LTripMiles = 2LTripMiles = 2LTripMiles = 3LTripMiles = 3L

P1

O

PersonMiles = 3LPersonMiles = 3LPersonMiles = 3LaTaxiMiles = LAVO = PersonMiles/aTaxiMiles = 3

PersonMiles = 3LaTaxiMiles = LAVO = PersonMiles/aTaxiMiles = 3

Elevator Analogy of an aTaxi StandTemporal Aggregation

Departure Delay: DD = 300 Seconds

KornhauserObrien

Johnson40 sec

HendersonLin

1:34

Popkin3:47

Samuels

4:50

HendersonLin

Young0:34

Popkin2:17

Elevator Analogy of an aTaxi Stand60 seconds later

ChristieMaddow

4:12

• By walking to a station/aTaxiStand– A what point does a walk distance makes the aTaxi

trip unattractive relative to one’s personal car?– ¼ mile ( 5 minute) max

• By using the rail system for some trips– Trips with at least one trip-end within a short walk

to a train station.– Trips to/from NYC or PHL

Spatial Aggregation

D

a PersonTrip from NYC (or PHL or any Pixel containing a Train station)

NYC

O

Princeton Train Station

NJ Transit

Rail Line to

NYC,

next Departu

re

aTaxiTrip

An aTaxiTrip {oYpixel, oXpixel, TrainArrivalTime, dYpixel, dXpixel, Exected: dTime}

• By walking to a station/aTaxiStand– A what point does a walk distance makes the aTaxi

trip unattractive relative to one’s personal car?– ¼ mile ( 5 minute) max

• By using the rail system for some trips– Trips with at least one trip end within a short walk

to a train station.– Trips to/from NYC or PHL

• By sharing rides with others that are basically going in my direction– No trip has more than 20% circuity added to its

trip time.

Spatial Aggregation

P1

P2

O

CD= 3p: Pixel ->3Pixels Ride-sharing

P1P5

OP3

CD= 3p: Pixel ->3Pixels Ride-sharing

– I just need a Trip File for some Local• {Precise O, Precise oTime, Precise D} • For All Trips!

– “Precise” Location: Within a Very Short Walk~ Parking Space -> Front Door

(Properly account for accessibility differences: conventionalAuto v aTaxi)

– “Precise” oTime : “to the second”(Properly account for how long one must wait around to

ride with someone else)

What about Level 4 Implications on Energy, Congestion, Environment?

• Motivation – • Publicly available TRAVEL Data do NOT contain:– Spatial precision• Where are people leaving from?• Where are people going?

– Temporal precision• At what time are they travelling?

Trip Synthesizer (Activity-Based)

Project Overview

Synthesize from 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 dayNJ_Resident file

Start with Publically available data:

Bergen County @ Block LevelCounty Population Census Blocks

Median Pop/ Block

Average Pop/Block

BER 907,128 11,116 58 81.6

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

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 TripsHome County

Trips TripMiles AverageTM# Miles Miles

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

Total 32,862,668 590,178,597 19.3

NJ_PersonTrip file

c

http://orfe.princeton.edu/~alaink/NJ_aTaxiOrf467F13/Orf467F13_NJ_TripFiles/MID-1_aTaxiDepAnalysis_300,SP.xlsx

Results

What about the whole country?

Public Schools in the USPublic Schools in the US

Nation-Wide BusinessesNation-Wide BusinessesRank 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 ????

Trip Files are Available If You want to Play



Discussion!

Conventional Cars Drive Urban/City Planning

Current State of Public Transport…• Not Good!:– Serves about 2% of all motorized trips– Passenger Miles (2007)*:

• 2.640x1012 Passenger Car; • 1.927x1012 SUV/Light Truck; • 0.052x1012 All Transit; • 0.006x1012 Amtrak

– Does a little better in “peak hour” and NYC • 5% commuter trips• NYC Met area contributes about half of all transit trips

– Financially it’s a “train wreck”

http://www.bts.gov/publications/national_transportation_statistics/2010/pdf/entire.pdf, Table1-37

Transit’s Fundamental Problem…• Transit is non-competitive to serve most travel demand

– Travel Demand (desire to go from A to B in a time window • A & B are walk accessible areas, typically:

– Very large number of very geographically diffused {A,B} pairs

• is diffused throughout the day with only modest concentration in morning and afternoon peak hours

• The conventionalAutomobile at “all” times Serves…– Essentially all {A,B} pairs demand-responsively within a reasonable

• Transit at “few” times during the day Serves…– a modest number of A & B on scheduled fixed routes– But very few {A,B} pairs within a reasonable

• Transit’s need for an expensive driver Forces it to only offer infrequent scheduled fixed route service between few {A,B} pairs– But… Transit can become demand-responsive serving many {A,B} if the driver is

made cheap and it utilizes existing roadway infrastructure.

0.25 mi.0.25 mi.

the darwinian evolution of smartdrivingcar s

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