BRUCE PEEVER, MA, MBA, CMC is a Director in KPMG’s Public Sector Advisory prac-tice. He works with cities, universities, and provincial governments across Cana-da helping them grow and surmount their unique chal-lenges. He is also a former

municipal executive with over 17 years of experience with a number of diff erent sized Canadian municipalities. He can be reached at <bpeever@kpmg.ca> or 905-523-2224.

In examining cities across Canada and the U.K., it has become apparent that there is a huge disruption on the doorstep of western cities – autonomous vehicles. While the topic of self-driving cars is gaining momentum in popular media, for most people it is background noise. It can be argued, however, that Autonomous Connected Electric and Shared (ACES) vehicles, a key concept in the autonomous vehicle industry, are much more real and imminent in our lives than most of us acknowledge.

Changes in Social BehavioursWhen I was studying at Queen’s

University in 1996, we had a professor present on the future of the video rental store industry. He told us to get all our money out of the industry because it was a dying business model. We scoffed, of course; after all, there was

AUTONOMOUS CARS

The Next Netflix?

by Bruce Peever

this new technology called DVDs and going to a Blockbuster video store on Friday night to scan the racks and racks of videos was part of our family ritual.

Online streaming was just fantasy … but, a year later, Netfl ix Inc. was founded. Starting out in the DVD-by-mail business, Netfl ix had expanded into streaming video service by 2007. Today, Netfl ix’s revenues are $8.3 bil-lion. Blockbuster fi led for bankruptcy in 2010.

The Friday night family ritual is now arguing over the online Netfl ix cata-logue. Over the course of 15 years, our social behaviours were entirely reset.

For autonomous vehicles, it is now 1997. And, like online streaming, we are struggling to accept self-driving cars as a plausible option. While cartoons with self-driving cars driving off cliffs are prevalent online, ACES are entirely

real and advancing in sophistication every year.

AutonomousAn autonomous car is a vehicle

that is capable of sensing its environ-ment and navigating without human input, relying instead on the processing power of its chips. The fact that we have reached this point in processing

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power is because of something called Moore’s Law.

In 1965, Gordon Moore, working then as the director of research and de-velopment at Fairchild Semiconductor, theorized that the number of transis-tors in a dense integrated circuit would double every two years. He extrapolated that computing would dramatically in-crease in power and decrease in relative cost at an exponential rate.

Fast forward to the next century, the doubling of processing power has al-lowed Apple to create incredibly power-ful phones relative to their work stations in only 10 years. But, this exponential increase in processing power has also allowed us to do something that we previously thought was science fiction – predictive analytics, or deep learning.

In the sports world, we call this Money Ball – where the future perfor-mance of athletes is predicted according to their statistical performance data. In the world of autonomous vehicles, we call it Deep Learning, and it has as-serted itself as a critical technology for the industry, accelerating the speed of innovation.

Think of your commute this morning and what was involved. In a 45-minute commute, you saw 235 road signs (25 of which you obeyed), three bicycles, 1,501 cars, four police cars, and 16 people texting and driving.

Imagine if you had to write this commute experience into a computer program for a car to deliver you to work autonomously. How many bil-lions of lines of code would be in-volved to make your morning commute successful?

Interestingly, the software in the latest Mercedes S Class has 15 times more lines of code than the software in a Boeing 787; and that still isn’t enough code.

What happens if someone breaks the law and runs the red light in front of

you? What if a bicycle comes speeding past you in the wrong direction? How many more millions of coded actions and permutations are necessary to get you to work?

The answer is that, instead of hu-mans programming all the logarithmic calculations, a machine builds on the data of its own experience. It is as if it was learning on its own. This is what is termed as Deep Learning, and is described as: I see, I think, I drive, I learn.1

Deep Learning systems are “trained” by repeatedly seeing more and more input data, and gradually optimizing the system’s ability to make an accurate prediction. As the “wrong-ness” of the system’s predictions is minimized, it becomes smarter and smarter until it can make highly accu-rate predictions. In this way, the system learns to recognize patterns in masses of unstructured raw digital data. The result: a system that can handle unex-pected situations and quickly suggest the best possible solution.

ConnectedBut, Deep Learning cars don’t work

in isolation. They need to be connected.In 1999, Neil Gross, a new sociol-

ogy professor at UBC, wrote an article for Business Week and forecast that: “In the next century, planet earth will don an electronic skin. It will use the inter-net as a scaffold to support and transmit its sensations.”

This is what we now call the Internet of Things or IoT. The IoT involves bil-lions of wirelessly interconnected de-vices communicating directly with one another. People, objects, animals, and even plants are given a unique identifier to transmit data over a network either human to human or human to computer without requiring human interaction.

It is expected that by 2020, the num-ber of connected devices will exceed 50

billion – and this is just starting to in-clude cars. The connectivity of autono-mous vehicles through the IoT involves principally four different sources:

V2P – vehicle to pedestrian – to in-form cars of pedestrians on a walkway; cars would communicate with pedes-trian smartphones.

V2N – vehicle to network – to in-form cars of traffic issues or disruptions – this is now so much a part of our car’s operations that we wonder how we op-erated without it.

V2I – vehicle to infrastructure – to sync the operation of the car to traf-fic infrastructure. This is just starting to emerge; Pittsburgh, Las Vegas, and the State of Virginia have all started to develop infrastructure to sync traffic infrastructure to vehicles.

V2V – vehicle to vehicle – to allow cars to work together on a highway.

Ultimately, it is connectivity that is the key to success for autonomous vehicles.

ElectricBut, what about the drive train? How

do autonomous vehicles relate to elec-tric cars?

The Paris Agreement under the United Nations Framework Convention on Climate Change, and the ongoing warming of our environment, are go-ing to shape market demand for electric cars. Consumer taste over the next five to 10 years will demand not only auton-omous functionality, but also an electric component to a car. Driving a fossil fuel car will increasingly be seen as a negative social behaviour. People will demand either pure electric or hybrid capability in their vehicles.

This viewpoint is reflected in the results of KPMG’s 2017 Global Auto-

Deep Learning systems are “trained” by repeatedly seeing more and more input

data, and gradually optimizing the system’s ability to make an accurate prediction.

1 See the report “I see. I think. I drive. (I learn) - How Deep Learning is revolutionizing the way we interact with our cars,” KPMG, March 2016.

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motive Executive Survey. Respondents report that electrified drivetrains will ac-count for 30 percent of global automo-tive production by 2023.

SharedFinally, we have shared vehicles.

Why is the concept of car sharing so important to ACES? The reason is that the prohibitive cost of autono-mous vehicles at the outset will mean that ACES are likely to be shared. That’s why companies such as Uber and Lyft have invested so heav-ily in autonomous vehicle technol-ogy. These companies will provide fleets of autonomous vehicles to the general population using a shared economy business model. Uber and Lyft are already in the transportation business and ACES are just a natural extension of their current business model – except that, with ACES, they will remove a substantial expense from their business model. Com-panies won’t have to pay a human driver, which accounts for most of their operational costs. They also see it as way to reduce the operational risk in their business model. Humans are an unpredictable operator prone to distractions and high-risk behav-iours. For example, as a result of the legalization of recreational marijuana

in Colorado, the City of Denver re-cently was unable to find enough qualified bus drivers who could pass a drug test.

Implementation & Public Infrastructure

So, let’s turn our attention to the implementation of autonomous vehicles and their potential impact on public infrastructure. Within a decade, we will have a new normal:

Phase 1 – The Training Wheels stage is already in our rear view mirror.

Phase 2 – First Gear is now fully engaged as partial driver substitution capabilities are introduced into various vehicle models.

Phase 3 – Acceleration is expected to result in complete autonomous op-eration features being introduced into vehicles by 2021.

Phase 4 – Full Speed is anticipated two decades out from now, when cars will be entirely autonomous and connect-ed with a passive driving experience.

Now, there is some divergence of perspective within the autonomous vehicle industry depending upon the background of the company. Tra-ditional car manufacturers (such as GM, Ford, and Volvo) are investing billions into ACES research. Their view is consistent with the traditional

perspective on autonomous vehicles; namely, it will be a four-stage devel-opment to full autonomous operation, occurring over two decades.

Autonomous vehicle companies that have a technology background (e.g., Google, Apple) are convinced that there is no need for Phase 3 of the ACES adoption timeline. They are working to roll out fully-autoto-mized vehicles within the next five to seven years and are of the opinion that autonomous vehicles with the ability for driver substitution involve too much risk.

Start Planning NowAt this point, it is too early to

tell which perspective will prevail. However, for the municipal officials who design, build, and operate public infrastructure, autonomous vehicles should be at the forefront of their thinking. Public infrastructure is built on a 30-to 50-year time frame and it is a certainty that autonomous vehicles will have changed our so-ciety within this time frame. Public authorities should start planning now to navigate through the issues of autonomous vehicles to ensure that our ACES dominated world is one designed to maximize social and eco-nomic benefit. MW

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