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The Darwinian Evolution of SmartDrivingCar s

The Darwinian Evolution of SmartDrivingCar s. by Alain L. Kornhauser, PhD Professor, Operations Research & Financial Engineering Director, Program in Transportation Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering) Princeton University Presented at ITS-NY

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The Darwinian Evolution of SmartDrivingCar s

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  1. The Darwinian Evolution of SmartDrivingCars by Alain L. Kornhauser, PhDProfessor, Operations Research & Financial EngineeringDirector, Program in Transportation Faculty Chair, PAVE (Princeton Autonomous Vehicle Engineering) Princeton University Presented at ITS-NY Twenty-first Annual Meeting Saratoga Springs, NY June 13, 2014

  2. AHS: Automated Highway Systems: 1939 - 1997 “Waterloo” may well be the word “System” Play video

  3. APM: Automated People Mover: 1968 - “Waterloo” limited to serve “Few to Few” demand

  4. PRT: Personal Rapid Transit: 1968 - Attempt to serve “Many to Many” but “Waterloo” may well be the word “Personal” & Exclusive Guideway?

  5. V2V: Connected Vehicles: 1997 - “Waterloo” may well be: Zero value until market penetration is high

  6. 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

  7. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Driverless Cars 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 & Trucks SmartDrivingCars

  8. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Driverless Cars

  9. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Driverless Cars

  10. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Driverless Cars

  11. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Driverless Cars

  12. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Driverless Cars

  13. What is a SmartDrivingCar? Preliminary Statement of Policy Concerning Driverless Cars

  14. Preliminary Statement of Policy Concerning Driverless Cars What the Levels Deliver: Levels 1 -> 2: Increased Safety, Comfort& Convenience Primarily an Insurance Discount Play Levels 3: Increased Pleasure,Safety,Comfort& Convenience An EnormousConsumer Play 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) A Corporate Utility/Fleet Play

  15. 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

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

  17. What About Buses?

  18. Use Autonomous Collision Avoidance Technology to Address a BIG CURRENT Transit Problem

  19. Good News! Travel by Bus is getting safer!

  20. Good News! Injuries have been trending down!

  21. Terrible News! Claims are going through the roof!

  22. 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”

  23. 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

  24. 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

  25. 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.

  26. Princeton University (with American Public Transit Association (APTA), Greater Cleveland Transit, and insurance pools from WA, CA, OH & VA) Pending $5M Grant from Federal Transit Administration The Initial Project: Focused on Research, Certification and Commercialization of SmartDriving Technology to Buses

  27. In those 6 months approximately: 39 Fatalities 7,200 Injuries $180M Claims “Level 2 Collision Avoidance Technology” Could cut those numbers in half Proposal Done: December 2, 2013: For next 6 months: Silence from FTA Why the delay in spending $5M to get the process started ???????

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

  29. What about Level 4 Implications on Energy, Congestion, Environment? • 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”?

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

  31. Kinds of RideSharing • “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”

  32. aTaxis and 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.

  33. Spatial Aggregation • 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! Elevator

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

  35. Pixelation of New Jersey Zoomed-In Grid of Mercer NJ State Grid

  36. Pixelating the State with half-mile Pixels xPixel = floor{108.907 * (longitude + 75.6)} yPixel = floor{138.2 * (latitude – 38.9))

  37. An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec) , } An aTaxiTrip {oYpixel, oXpixel, oTime (Hr:Min:Sec) ,dYpixel, dXpixel, Exected: dTime} a PersonTrip {oLat, oLon, oTime (Hr:Min:Sec) ,dLat, dLon, Exected: dTime} P1 D O O

  38. Common Destination (CD) CD=1p: Pixel -> Pixel (p->p) Ride-sharing P1 O TripMiles = L TripMiles = 2L TripMiles = 3L

  39. P1 O PersonMiles = 3L PersonMiles = 3L aTaxiMiles = L AVO = PersonMiles/aTaxiMiles = 3

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

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

  42. Spatial Aggregation • 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

  43. a PersonTrip from NYC (or PHL or any Pixel containing a Train station) An aTaxiTrip {oYpixel, oXpixel, TrainArrivalTime, dYpixel, dXpixel, Exected: dTime} NJ Transit Rail Line to NYC, next Departure NYC D O aTaxiTrip Princeton Train Station

  44. Spatial Aggregation • 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.

  45. CD= 3p: Pixel ->3Pixels Ride-sharing P1 O P2

  46. CD= 3p: Pixel ->3Pixels Ride-sharing P5 P1 O P3

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