http://www.SmartDrivingCar.com

Sunday, August 11, 2013 http://orfe.princeton.edu/~alaink/SmartDrivingCars/SDC081113.pdf

Uncongested Mobility for All: NJ’s Area-wide aTaxi System: Part 2, aTaxi Operational Philosophies and Service Performance Opportunities My students and I have been conducting a quantitative assessment of the mobility implications of autonomousTaxis (aTaxis), the ultimate in Smart Driving Cars. The task was simple: How well could a truly safe fleet of self-driving cars serve the demand for personal mobility? Rather than just focus on the mobility needs of cities, or suburbs the decision was to assess the full spectrum of today’s land uses. New Jersey was selected not only because we live here, but also because it embraces essentially all uses of land from extremely rural farms and preserved spaces, through a wide variety of suburban developments to both old and new high density urban living. The May 14, 2013 issue of Smart Driving Cars described how we synthesized each of the 32+ million trips made by New Jersey’s 9 million inhabitants and out of state workers that interact with the land uses on a typical weekday. Today most of these trips are taken by the personal automobile or walking. Only 0.90 million are served by NJ Transit, of which 0.54 are by bus, 0.28 by rail and 0.07 by light rail. School buses served more trips than mass transit (1.4 million, est. by The Public Purpose in 1994).

This issue of SDC reports on how a large fleet of aTaxis complemented by the existing NJ Transit Rail System, walking and biking could serve all the 32+ million daily trips. The aTaxis are envisioned to operate throughout New Jersey’s existing roadways, tunnels, and bridges. They are complemented by NJ Transit’s existing commuter rail facilities. This multi-modal system would serve all long trips that wouldn’t otherwise be served by walking or cycling.

Walking & Cycling Use: About 1.94 million of the 32.86 million (~5.9%) of the trips on a typical weekday are less than about 0.5 miles (<10 minute walk). All are assumed to be taken by walking or cycling.

Rail Operation & Ridership: It was assumed that commuter trains operated on NJ Transit’s rail lines on 15-minute headways between New York and Newark and Hoboken, leaving New York on the quarter hour. Those trains leaving on the half hour continue beyond Newark to the far reaches of the current rail lines. Trains were assumed to leave Philadelphia on the half hour and run to New York and Atlantic City. All trips originating or terminating in New York or Philadelphia reach those locations by commuter train from/to rail stations that are closest to the ultimate other end of each of those trips. Approximately 0.9 million (~2.7%) use the rail service to/from NYC and 0.2 million (~0.6%) to/from PHL. Of those, only about 5% originate/terminate from a location that is within walking distance of a station. The rest (~95%) use aTaxis to get from/to the rail station nearest their ultimate origin/destination. Other than trips to/from NYC & PHL, the Rail System serves the 1.3 million (~4.0%) trips that originate and terminate within walk distance of a rail station. These use the Rail System for their entire trip. An additional 3.7 million (~11.2%) trips are sufficiently long (greater than 5 miles) and have only one end within walking distance of a rail station. These are deemed to use the rail<->aTaxi systems in a multi-modal combination.

Thus, 1.4 million (~4.3%) of daily trips are served exclusively by the existing NJ Transit Commuter Rail System and another 4.85 million (~14.8%) utilize the Rail System in a multimodal combination. aTaxis collect/distribute the trips to/from one end of the rail service. Walk is used at the other end. Combined, this produces a daily rail ridership of 6.25 million. We realize that this is 22 times larger than today’s ridership; however, this is a reflection of the substantial trip generating activity that currently exists within a half mile of NJ Transit’s existing stations and the large number of trips that could be attracted to the Rail System if there existed an efficient collection/distribution system that would bring the trips to to/from the stations. (aTaxi<->Rail<->aTaxi attracted zero trips.)

aTaxi System & Ridership: Envisioned was the existence of a large fleet of driverless autonomousTaxis that are managed such that they are available within at most a short walk (< 5 minute) of any trip origin or rail station and utilize the existing roadway infrastructure to reach any trip destination, including each rail station, within at most a short walk distance. Such an aTaxi system would serve 24.82 million (75.5%) trips exclusively plus an additional 4.85 million (14.8%) trips multi-modally with the Rail System. A summary of the daily trip types is presented in Table 1 below.

Table 1. Trips by mode on typical weekday in New Jersey Item Walk&Cycle to/fr NYC to/fr PHL Train Only aTaxi+Train aTaxi Only Rail Total aTaxi Total Total Trips

Trips/Day x106 1.94 0.9 0.2 1.3 3.7 24.82 6.1 29.6 32.86 % Daily Trips 5.9% 2.7% 0.6% 4.0% 11.2% 75.5% 18.6% 90.1% 100%

aTaxi Operational Costs: aTaxis have the potential to offer on-demand direct origin2destination service 24x7x365 to

everyone without incurring the chauffeur cost that is required of such service today. If offered on a purely personal door2door basis, the result is one vehicle to each trip. While this would be the ultimate form of mobility it would exacerbate the cost, congestion, energy and environmental challenges of our current automobile focused mobility system. However, elimination of the chauffeur labor cost allows aTaxis to wait for passengers without incurring any operational costs. Moreover, the opportunity to serve small groups, single passengers and even reposition-empty without incurring any labor costs offers the opportunity to revolutionize “Public Transit”. It is the operational cost of the driver that relegates conventional transit to be “mass” transit by necessitating transit companies to buy and operate big buses and forcing conventional automated transit to operate in dedicated guideways whose capital cost make them practical in only extremely high demand corridors. Creating automated vehicles that can operate harmoniously on existing roadways eliminates the capital cost burden of automated transit. This allows the automated system to continue to serve the needs of large groups whose demand happens to be temporally and spatially correlated, as well as small groups and even individuals that need to travel to and from locations at times when no one else wishes, thus truly providing “mass” transit to everyone, the masses and the individual.

aTaxi Operational Philosophies, PRT & SPT: Just like conventional taxi systems, aTaxis could offer various levels of mobility depending on their operational philosophy. Each philosophy would correspondingly impose its own level of congestion, energy consumption, environmental impact and financial costs. For example, a “personal-hail-responsive” aTaxi operational philosophy, as provided today by conventional taxis in Manhattan, can be expected to offer the highest level of demand responsive Point2Point mobility. This service is even better than the personal automobile since it doesn’t impose a parking hassle nor vehicle ownership on the traveler; however, it does impose a substantial empty vehicle cruise and repositioning requirement. Its personal single-ride restriction doesn’t enable unrelated travelers to share the taxi, thus making it expensive, energy wasting and unable to address congestion issues. If instead aTaxis operated like conventional taxis in Las Vegas where taxis are “hailable” only from designated taxi stands, then unrelated taxi sharing can be fostered as happens today especially during peak periods at the hotels and airport. Such aTaxi stands would be equivalent to conventional transit stations except aTaxis could wait at stands for riders and offer direct to destination service over existing streets with few if any intermediate stops. Such service resembles that offered by Personal Rapid Transit (PRT) and is termed the PRT operating philosophy.

Ride sharing can also be encouraged by a “Smart Para-Transit” (SPT) operating philosophy whereby the aTaxi sweeps up from a local area passengers that happen to be destined to a common destination at about the same time (trips that are correlated spatially and temporally, see Fig. 1 below). Since it is the vehicle that is performing the sweep operation rather than the individual traveler doing it by walking in the PRT philosophy, SPT affords the matching of trips over a larger spatial area while providing a comparable level of service. However, SPT requires active management, coordination and discipline to properly route vehicle pickups, a task that is greatly simplified in PRT which only requires that passenger to be directed to the correct waiting vehicle.

Ridesharing Opportunities of aTaxis: A variety of aTaxi PRT and SPT operational philosophies were simulated to estimate the physical and environmental requirements they need to serve all of the personal mobility needs of a typical New Jersey weekday. For the PRT aTaxi systems it was assumed that aTaxi stands existed within a 5 minute walk of any trip origin or destination. To that end, the State was “pixelated” into half-mile squares. All travelers within a pixel were assumed to walk to the aTaxi stand located at the center of the pixel and be prepared to enter the proper waiting aTaxi at the trip’s departure time. Once the first passenger entered a waiting aTaxi, the aTaxi would wait a fixed amount of time (delay departure (DD)) for any additional passengers that were destined to the same or common destination (CD), much as elevators stay open waiting for additional passengers. The longer the wait, the worse the level of service, but the greater the opportunity to share rides. The highest level of service is offered if the

common destination is the same pixel (CD=1) delivering non-stop direct service to all passengers. If passenger destined to other pixels share the departing vehicle, then some passengers will have a more circuitous trip and intermediate stops imposed. Ridesharing was only imposed on destinations that incurred less than a 20% circuity on any trip.

In the simulation, the first passenger arrival sets the aTaxi destination and the departure time is delayed by DD. Any other traveler arriving before departure and destined to either the same destination for CD =1 or any destination that doesn’t violate the CD or circuity constraint shares the vehicle. Recorded for each departed vehicle is its Departure Occupancy (DO), the Passenger Miles (PM) that it served and the Vehicle Miles (VM) that it traveled to serve those trips. Average Vehicle Occupancy AVO, the fundamental performance measure, is simply the ratio of passenger miles to vehicle miles, AVO=PM/VM. (It should be noted that for today’s automobile ride sharing is largely by people who are “along for the ride” rather than among people that just happen to be traveling from the same place to the same place at the same time. Unfortunately, the trip synthesizer that generated New Jersey’s trips was not sophisticated enough to have replicated the propensity by which individuals change their desired destinations and departure time so as to correlate with those that they wish to join in the trip end activity and “go along for the ride” or be “chauffeured”. If so modeled, the aTaxi AVO would be correspondingly higher or alternatively, a current automobile AVO=1.0 should be used as the comparative measure.)

Fig 1 Fig 2 Fig 3 Fig 2 above summarizes the ridesharing opportunities of the aTaxi PRT operating philosophy. Even for waits (DD) up to 5

minutes the daily, Jersey-wide AVO is modest at best. This is reflective of the fact that, for even the most densely populated state in the nation, overall trip patterns are enormously diffuse spatially and temporally. As a whole, even if one is willing to wait a “long” time (5 minutes), opportunities to share rides between common walk locations (CD=1) by otherwise uncorrelated travelers is rare. It doesn’t achieve even a value of 1.25 (e.g. 25% of the vehicles serving two passengers while the rest serve only one). Thus, aTaxis using a PRT operating philosophy offering only non-stop trips (CD=1) provides excellent mobility, but contributes marginal energy and environmental savings. During off peak hour and in off peak directions, there are no opportunities for ridesharing. In peak hours and peak directions, AVO spikes to offer values greater than 2.0 that eliminate much of New Jersey’s traffic congestion.

If service is relaxed even slightly to allow ridesharing for trips that have two destinations, thus incurring on one of the trips an intermediate stop and at most a 20% circuity (CD=2), then the AVO reaches levels of 2.0 for the entire day. Most trips are personal direct origin to destination, but the flexibility of destination allows for more sharing in the peak hours and peak directions. Such an operation would halve operating, energy and pollution costs and eliminate essentially all congestion while still providing an extremely high level of service. If additional compatible destinations (CD>2) are enabled the ridesharing propensity begins to saturate and doesn’t allow AVO to get much above 3.0 because the originating capture area remains a single half-mile square pixel and trips are diffuse in time. This suggests that the appropriate PRT operational policy would be a departure delay of 3 minutes (DD=3) with at most 3 common destinations (CD=3) in any departing vehicle delivering a 2.25 overall daily AVO.

Ridesharing Opportunities of aTaxis, SPT Operational Philosophy: A simple version of the SPT operational philosophy was implemented by restricting the trip pickup and to a spatial neighborhood around the first trip assigned to a new aTaxi. It was deemed that the time and inconvenience imposed by a sweeping operation over a square area 1.5 miles wide was comparable to the walk and delay disutility associated with a 0.5 mile walk pixel of the PRT philosophy. SPT would thus sweep up any trip that originated within any of the nine (9) neighboring half-mile pixels encompassing the first rider’s origin pixel and destined to any of the 9 pixels encompassing the first rider’s destination. An SPT system sweeping the origin area for up to 5 minutes, picking up passengers to a common 9-neighboring pixel destination that would achieve a 2.16 overall AVO while offering comparable mobility and societal benefits as the DD=3, CD=3 PRT operational philosophy.

Vehicle size: To serve these demands vehicles of various sizes would be utilized. Most of the fleet would be two or four passenger vehicles since most vehicle trips have little or no ridesharing. Some slightly larger 6-8 passenger vehicles would be

dispatched and made available to serve destinations that tend to have expected larger demands. A few larger 20-40 passenger vehicles would be retained to serve high demands at transit stations, the emptying of large venues and departures from large schools and places of employment. Fig. 4 below plots the impact on AVO if larger groups cannot be accommodated in a single vehicle and must slit up and take multiple vehicles. (Fig 4 is for SPT; however, PRT has a similar behavior.) As long as there exists vehicle of capacity of 12 riders and they are managed efficiently, then very few larger vehicles are not needed.

Fleet size: Even with the orchestration of vehicle sizes with expected demand, the wide variation in demand implies that many of the vehicles needed during peak times must be parked unused during the rest of the day. The absolute minimum fleet size is the maximum number of vehicles serving trips at any time during the day. Fig 5 below plots the maximum number of aTaxis engaged in serving trips during each of the half-hour period during the day. As can be seen at least 1.6 million vehicles are needed to serve the peak demand occurring in the late afternoon. Additionally, even during peak periods, some vehicles will need to be repositioned to origin locations for which there are insufficient vehicles delivering passengers. This empty vehicle repositioning requires the fleet to be larger than just the maximum number of vehicles serving passengers at any time during the day. We haven’t completed our analysis of optimal repositioning strategies, although it is expected that repositioning will be minimal during off peak periods when there exists excess vehicles available to “wait around’ for the next rider. However, during peak periods it will be necessary to have at least 10% extra vehicles available to move empty from areas that have a net surplus of arrivals to areas that expect to have a net deficit. Thus, a fleet size of nearly 1.8 million aTaxis is expected to be required. As a comparison, New Jersey currently has over 4 million registered cars and light trucks.

Fig 4 Fig5

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Italian low-cost driverless car prototype takes to the roads “On July 13 a low-cost driverless car developed by Italian computer vision expert Alberto Broggi navigated the roads of Parma, Italy in public traffic. The route included rural and urban roads, two freeway segments, traffic lights and roundabouts. This is a major accomplishment because almost all current autonomous cars use an extremely expensive LIDAR sensor (which costs between 30000 and 70000 USD). Broggi’s car, in contrast, relies on an array of low-cost sensors including stereo cameras and several low-cost laser sensors. All sensors are hidden from view…” Read More The video is a MUST Watch. The last couple of minutes are THE most impressive driverless car demo to date, Alain. *****************************************************************************

One of the most cogent comments made at the recent TRB workshop on Road Automation was made by Adriano Alessandrini. He warned that the legal framework for “Level 4 Driverless” operation like that proposed by California DMV that focuses on the licencing of drivers is actually dangerous and would impede the development of Level 4. As I’ve pointed out above, the biggest opportunity to capture large societal benefits and revolutionize mass transit is to achieve Level 4 automation operating harmoniously on public streets mixed with

conventional vehicles. Since this implied driverless operation by the Level 4 automated vehicles, legislation regulating such Level 4 automation can’t be aimed at licensing drivers! Else, it is mandating away the fundamental element that makes the technology most useful. Adriano compared this dilemma with what he experienced with the European debate around the Vienna convention and its small sentence on the responsibilities of the driver. This connection of a driver with driverless vehicles is blocking all legal attempts to implement driverless vehicles on existing roads to the point that Europeans need to go around it using the rail legal framework. At least for those of us who believe the only real innovation is in Level 4, we need to immediately put pressure on legislators so that they define a legal framework which recognizes that Level 4 automation does not need nor is enhanced by having drivers involved with its operation. Luckily, elevators and automated people movers have been enabled in buildings and airports without the requirement of drivers. DMVs should begin with that perspective and evolve a legal framework that enables and encourages Level 4 driverless implementation on public roads in mixed traffic and reinforce that a driver is unnecessary. Alain *****************************************************************************

Maryland to seek private firm for Purple Line project “Maryland will seek a private company to build and operate a planned $2.2 billion light-rail Purple Line, marking the first time the state has used such financing on a public transit project…. Maryland officials are counting on the federal government to cover about $900 million of the project’s construction cost and will look for contributions from local governments and the private sector as well, said Erin Henson, a spokeswoman for the Department of Transportation. Construction could begin as early as 2015…” Read more This is an example of a very capital intensive system that has been in the planning stages for a long time and is about to be blind-sided by the fast emerging opportunities of automated vehicles as an alternative. It is just this kind of project that needs to take a step back to make sure that it is not obsolete just at the time that the ribbon is being cut to start operations. If Google is successful in its pronouncements that driverless vehicles operating side-by-side on existing roadways is a reality by 2018, then indeed this system will be obsolete at ribbon cutting. It would behoove a private company to take a look and propose as an alternate to build an exclusive 16 mile segregated narrow 2-way roadway instead or a railway, elevated where it needs to be, along the same right-of-way, with the 20 stations at grade and off-line. Such a roadway would allow large existing automated vehicles to offer conventional line haul (light-rail style) service as well higher quality skip-stop service (because of the off-line stations). The at-grade off-line stations would allow for an efficient interface to the existing surface streets, allowing for access and egress to the emerging fleet of “Google cars”. Thus this facility could accommodate and serve as the high capacity spine as originally intended as well as contribute and serve the much larger transit opportunities that would emerge by providing automated transit service to locations in the surrounding communities using the web on existing neighboring streets. Certainly the $2.2 billion budget is enough to build the simple guideway and ramps. Automated vehicles certified to operate in dedicated roadways exist today and provide better and cheaper service than light rail. With the added hedge that such a system would substantially complement a rapidly emerging technology that is likely to obsolete the planned technology, it is imperative that a fresh look be taken at this proposal. Alain *****************************************************************************

Recent presentations and papers:

Want to make the road a safer place? Get rid of the drivers

Smart Driving Technology: How it can Substantially Enhance the Quality of Life in New Jersey

by Dr. Jerome Lutin: Application of Autonomous Driving Technology to Transit -Functional Capabilities for Safety and Capacity

July 2013 with Jerome M. Lutin and Eva Lerner-Lam The Revolutionary Development of Self-Driving Vehicles and Implications for the Transportation Engineering Profession

Submitted to 2014 annual meeting: With Scott Le Vine Shannon McDonald and Stan Young: Deliberations from an Expert Workshop on Vehicle Automation, Public Transportation, and Shared Mobility with Chris Brownell: Autonomous Taxi Networks: a fleet size and cost comparison between two emerging transportation models and the conventional automobile in the state of New Jersey with Talal Mufti and JingKang Gao: Synthesis of Spatially & Temporally Disaggregate Person Trip Demand with Jaison Zacharia, JingKang Gao and Talal Mufti: Uncongested Mobility for All: A Proposal for an Area Wide Autonomous Taxi System in New Jersey with Jerome Lutin: Application of Autonomous Driving Technology to Transit - Functional Capabilities for Safety and Capacity *****************************************************************************

The Ethics of Saving Lives With Autonomous Cars are Far Murkier… “…Let’s say that autonomous cars slash overall traffic-fatality rates by half. So instead of 32,000 drivers, passengers, and pedestrians killed every year, robotic vehicles save 16,000 lives per year and prevent many more injuries. But here’s the thing. Those 16,000 lives are unlikely to all be the same ones lost in an alternate world without robot cars. When we say autonomous cars can slash fatality rates by half, we really mean that they can save a net total of 16,000 lives a year: for example, saving 20,000 people but still being implicated in 4,000 new deaths. There’s something troubling about that, as is usually the case when there’s a sacrifice or “trading” of lives…” Read more *****************************************************************************

Insurance falls on Volvo cars with City Safety (in UK) Published 24 July 2013

“…Depending on the model, Volvo is claiming customers can expect to save up to £161.81 ($250.97) ” Read more *****************************************************************************

Car-sharing program taking off in Miami-Dade “…One year ago, Car2go and the Miami Parking Authority signed a contract to pilot the car-sharing program in Miami, allowing the company to park its vehicle on city streets. Since then, about 16,000 people have signed up locally…” Read more *****************************************************************************

Hands-On with the 'Automatic' Connected Driving Assistant System “ Announced earlier this year, the Automatic Smart Driving Assistant is a Bluetooth 4.0 device that plugs in to your car's OBD-II port. Typically found somewhere under the steering wheel of every vehicle made after 1996 in the USA, the OBD-II port provides all sorts of useful diagnostic information…once you start driving, it begins tracking everything you're doing. Data points captured include how long you were driving (both in time and distance), your miles per gallon, how

many times you both braked or accelerated too hard, and how many minutes you were driving over 70 miles per hour. Your route is also saved and plotted on a map, and by tracking local gas prices the app computes how much each trip cost you….” Read more *************************************************************************

News Release | July 3, 2012 Crash avoidance features reduce crashes, insurance claim study shows; autonomous braking and adaptive headlights yield biggest benefits video An early crop of advanced crash avoidance technologies includes some clear success stories when it comes to preventing crashes, insurance claim analyses by the Highway Loss Data Institute (HLDI) show. Forward collision avoidance systems, particularly those that can brake autonomously, along with adaptive headlights, which shift direction as the driver steers, show the biggest crash reductions. Lane departure warning appears to hurt, rather than help, though it's not clear why, and other systems, such as blind spot detection and park assist, aren't showing clear effects on crash patterns yet. Read more: They are working *****************************************************************************

Smart Driving Cars Wednesday, July 24, 2013

Summary of: July 16-19, ’13 Stanford University, Palo Alto| Agenda | Breakout Sessions | Demonstrations As anticipated, the workshop was excellent. It attracted over 300 attendees most of whom not only stayed engaged for the whole 3 days but participated in both pre-and post-workshop activities. While organized by the Transportation Research Board (TRB) this workshop received no government funding and was supported entirely by the participants. All organizers, speakers and attendees paid the registration fee which was lowered substantially by support from the following Benefactors:

read more *****************************************************************************

Smart Driving Cars Saturday, July 13, 2013

Mobileye, a Maker of Automated Driving Systems, Raises $400 Million *****************************************************************************

Smart Driving Cars Thursday, June 30, 2013

State Senator Thomas H. Kean, Jr. : autonomous vehicle legislation. *****************************************************************************

Smart Driving Cars Wednesday, June 16, 2013

Sneak peek at what Dr. Jerome Lutin, former Senior Director at NJ Transit will say at the symposium entitled: Application of Autonomous Driving Technology to Transit - Functional capabilities for Safety and Capacity USA Industry-wide average of 63,000 bus crashes per year, resulting in 14,000 injuries and 351 fatalities.

• NJ TRANSIT had four pedestrian fatalities in 2012 and 217 injured in 34 bus collisions and 163 incidents. • NJ TRANSIT reported paying out $43.2 million in injury and damage claims in FY 2012. Assuming 33% of claims

are allocated to Bus Operations on the basis of passenger miles suggests $14.0 million in bus claims. With an owned and contracted fleet totaling 2,403 buses (excluding 624 buses leased to private carriers), the average claims cost is estimated $6,404. per vehicle for the year 2012.

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Thursday, June 6, 2013

NHTSA Preliminary Statement of Policy Re: Automated Vehicles http://orfe.princeton.edu/~alaink/SmartDrivingCars/Automated_Vehicles_Policy.pdf contains the details of this preliminary policy. I highly recommend that you read it. My interpretation: http://orfe.princeton.edu/~alaink/SmartDrivingCars/CommentOnNHTSA_PrelimStatement.pdf

Early Estimate of Motor Vehicle Traffic Fa talities in 2012 Not a pretty picture. Early estimates show a 5.3% increase in fatalities over ’11 to 34,080 due to a very large YoY increase in Q1 (12.6%) and a an extremely large increase of greater than 15% in the Northeast region. While some of this may be attributable to increased VMT, Fatalities per VMT also increased. http://orfe.princeton.edu/~alaink/SmartDrivingCars/PDFs/EarlySafetyFacts2012NHTSA.pdf

Smart Driving Cars Wednesday, May 29, 2013

“Intelligent Drive: networked with all senses

The Road Ahead: Advanced Vehicle Technology and its Implications May 15 2013 2:30 PM Russell Senate Office Building - 253 Archived webcast Starts @ 26:20 AlainK Analysis *****************************************************************************

Smart Driving Cars

Thursday, May 14, 2013

Mercedes “Hard to Imagine” Commercial. I watch little TV, but I am pleased that Mercedes continues to hit prime spots with this ground-breaking commercial. NBC had it right after the running of the Kentucky Derby and it aired several times in the New York market during the Rangers Playoff games. They are even playing this spot on during the Daily Show. They must be seeing traction. *****************************************************************************

Uncongested Mobility for All: NJ’s Area-wide aTaxi System Part 1, The Demand for Mobility This year my students and I have been conducting a quantitative assessment of the mobility implications of the ultimate in Smart Driving Cars. The task was simple: How well could a truly safe fleet of self-driving cars serve the full spectrum of personal mobility needs… *****************************************************************************

Smart Driving Cars Thursday, May 2, 2013

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Smart Driving Cars Friday, April 25, 2013

Mercedes is 1st Mover and Lifts Bar with ‘14 Mercedes E-Class Safety Features Supported by the following TV Commercials (If you haven’t seen them on TV they are worth watching “

“Hard to Imagine” Commercial “Clown” Commercial From the Public Sector: My response to the US DoT on Surface Transportation System Automation (http://orfe.princeton.edu/~alaink/SmartDrivingCars/Kornhauser_%20Response2AutomationRfI.pdf *****************************************************************************

Smart Driving Cars Friday, April 19, 2013

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Monday, April14, 2013

The Business Case for SmartDrivingCars: For the consumer, SmartDrivingCars have three main values: increased safety, comfort and convenience. Of these safety is most easily quantified because damages are largely adjudicated in monetary terms. AAA estimates that traffic fatalities and injuries amounted to $256B in 2011, or a cost of about $1,328 in ‘05 dollars for each licensed driver. Of this amount approximately 50% ($664) is paid by private insurance, the pass-through portion of insurance premiums. Individual crash victims absorb 26% ($340) of the cost

(basically the deductible of what the insured has to absorb if involved in an accident), other 3rd parties absorb 14% ($185), the Federal treasury absorbs 6% ($80) and local municipalities 4% ($50). Google’s simulation of the operation of its self-driving car on the range of real crash scenarios resulted in a forecast of 81% fewer fatalities and 65% fewer injuries. This substantial reduction in car crashes would save in the US $183 billion annually. Moreover, these safety improvements would be enjoyed proportionally by each owner/user of a Google car. Thus, the insurer of the average licensed driver switching to a “Google car” could expect to reduce its pass-through liabilities by an average of $475 per year. Since these are simply pass-though dollars, one could expect that an insurance price-leader might readily offer discounts of up to, say, $450, keeping the expected remaining $25 for its “generosity”. The Google car user would also forgo $247 in expected “deductible self-insured” obligations.

The $450 insurance discount could readily finance, if not the expensive Google “lidars”, the lower cost radars and cameras contemplated by the auto industry for its initial wave of automated lane keeping and “always-on” collision monitoring and avoidance systems. For example, the Mercedes “jam-assist” system is expected to be available on 2014 models as a $3,000 “driver assistance safety option”. While jam-assist doesn’t have all of the features of a Google car, it may be able to capture as much as two-thirds of the safety benefits through the collisions that jam-assist can be expected to avoid during the car’s lifetime. If so proven, then the $300 discount that Flo, or the Gecko, or Good Hands or the General or some other insuer can readily offer would essentially finance this $3,000 safety feature. In fact Flo should escort you to the Mercedes dealer and pay for the option if you agree to buy a Mercedes and continue your current policy payments. (Remember, in giving Mercedes $300 per year over say 12 years, she is also keeping that $25 “generosity” for her effort, so she is happy.) In addition to substantially reducing the probability that this car is going to kill you, what’s in it for you? Well, how about the two-thirds of the $247 self-insurance expected obligation that you would avoid each year. More importantly you get the anxiety-relief that flows from having driving assistance while traveling in some of the most tedious, boring and unpleasant roadway conditions. Finally, society wins because we can’t really place a value on the injuries and fatalities that will be prevented. They are priceless!

Going all the way with Google Cars (or even just two thirds of the way with “jam-assist”) would mean for New Jersey an annual avoidance of 500 (340) fatalities and 28,000 (19,000) injuries “valued” at $3.55 ($2.38) Billion per year.

We MUST make this happen. Everybody wins. *****************************************************************************

Smart Driving Cars Monday, March 31, 2013

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Monday, March 25, 2013 *****************************************************************************

Smart Driving Cars Monday, March 18, 2013

European Update: Workshop: Automation in Road Transport (contains links to participants & presentations) ….As background if you haven’t read it: from June 29,2011: Definition of necessary vehicle and infrastructure systems for Automated Driving Final report SMART 2010/0064

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Smart Driving Cars Monday, March 11, 2013

Best videos from Workshop: Automation in Road Transport (contains links to participants & presentations)

Automated Steering Avoidance of imminent collision on Frozen Lake done Feb 23, 2013. Videos of automated collision avoidance maneuvers involving only steering followed by Volvo Platooning video

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Smart Driving Cars Monday, March 4, 2013

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Smart Driving Cars Thursday, February 28, 2013

This is BIG!!!

Continental and BMW Group Working Together to Develop Freeway-Grade Highly Automated Driving BMW Press Release Continental Press Release This is BIG, not only because they have “an agreement to jointly develop an electronic co-pilot for this purpose”, but because…

• It aligns a component supplier with a manufacturer. Where does this leave Daimler and VW/Audi? To join up with Bosch?? What about Delphi? Join back with GM on this one?? Where does this leave the other manufacturers; will they align? The competitive race to attract consumers to the showroom has really heated up.

• They’ve realized that safety is now clothed in comfort & convenience. Together, they make a powerful message to the car buying public.

This technology will draw people into the showrooms. The wake-up call was delivered by the emergent competitor, , rather than government edicts or rule-makings. “… [I]n capitalist reality…, it is not [price] competition which counts but the competition from the new commodity, the new technology…- competition which commands a decisive cost or quality advantage and which strikes not at the margins of the profits and the outputs of the existing firms but at their foundations and their very lives.” Joseph A Shumpeter (1883-1950)

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Smart Driving Cars Thursday, February 21, 2013

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Smart Driving Cars Thursday, February 14, 2013

Smart Driving Cars

Friday, February 8, 2013 Smart Driving Cars

Thursday, February 7, 2013 Smart Driving Cars

Thursday, January 31, 2013

mailto:alaink@princeton.edu