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Driving Disruption

Disruption in any industry is exciting, but it’s especially exciting in the field of autonomous driving. Autonomous driving or “self-driving cars” seems like something out of a movie, yet it is very close to becoming a reality in our everyday lives. From small start-ups like Drive.ai, a start-up company that looks to fix the uncertainties of autonomous driving through deep learning, to Fortune 500 companies like General Motors, everyone is racing in this highly competitive field to see who will be the first to take a large chunk of the market share. Automakers such as Tesla, Mercedes-Benz, General Motors, Volvo and many more are all working around the clock to be the first to capture what will soon be a multi-billion-dollar industry of autonomous vehicles.

So why should you believe all the hype surrounding this disruption of the auto industry? Surely a computer can’t drive better than a human. Well, let’s break down how exactly autonomous driving works today and what is required for it to work in the future, along with the progress that has already been made. Obviously, self-driving cars are very complex, so I will only give a brief description of how they operate. In a nut shell, autonomous cars rely on cameras, radars, and lasers in order to understand the distance of objects including road hazards and other vehicles, along with a number of other things. The cars then use deep learning to help make instant decisions based on the mass amounts of data it has accumulated; think of this as the brain of the car. Robotics combined with what I have already talked about complete the job by allowing the car to steer, accelerate, break, etc. The final missing piece of the puzzle is of course navigation, which is crucial for feeding information on the roads it’s driving on. Once again, this is a bare-bones summary of the autonomous car, and I highly recommend further personal research to understand how exactly they function. Here are two links that can help you better understand it. What is deep learning / Explaining How End-to-End Deep Learning Steers a Self Driving Car

Progress is heating up very quickly for self-driving cars from the first attempt in 2004. DARPA hosted an autonomous driving competition in 2004 and through a collaboration of top academics, industry leaders, and other experts attempted to drive an autonomous car through a 132-mile course. Out of the 15 teams, none of them succeeded with the best distance being a whopping 7.4 miles. Fast forward a year later and a team from Stanford completed the 132-mile course in six hours and fifty-four minutes. Fast forward again to 2017 and you can purchase your very own automobile with self-driving features.

Who is leading the charge?

When you think about self-driving cars, you usually think of the industry leaders like Tesla, Audi, Mercedes-Benz, BMW, Volvo, and General Motors, but who is actually making the software and chips that make all this capable? Companies like Nvidia, Intel & Mobil eye(Acquired), Bosch, and many more smaller companies are all key players in the driverless car industry. Without the tech companies working hand in hand with automakers to produce highly capable hardware and software, autonomous driving would still be far from a reality.  Below you can see the five-year performance of the top automakers and tech companies focused on autonomous driving.

General Motors (GM)
– designs, manufactures, markets, and distributes vehicles and vehicle parts, and sells financial services. Acquired self-driving start-up Cruise Automation for $1 billion within the last year.

Tesla (TSLA) 
– an American automaker, energy storage company, and solar panel manufacturer based in Palo Alto, California. Currently, has cars that offer semi-autonomous features.

Ford (F) 
– American auto manufacturer. Claims they will have a fully autonomous vehicle in operation by 2021 which they plan to sell directly to ride hailing services such as Uber & Lyft. 

Alphabet (GOOGL)
– Owns Waymo, Google’s autonomous car subsidiary. Their cars have logged over 3 million miles of autonomous driving on public roads. 

Nvidia (NVDA)
-manufactures the chips that power the computers of most autonomous driving cars, arguably the backbone of future of self-driving cars, especially for Tesla.

Intel (INTC)
– Manufacturers the chips that power some self-driving cars, recently acquired Mobileye (MBLY) [See below] which specialized in autonomous driving. Toyota also recently announced a partnership with Intel to develop a self-driving car ecosystem.  

Mobileye (MBLY) Acquired by Intel


Job Displacement

The first wave of displacement in the auto sector came around when Uber, Lyft, and other ride sharing services emerged causing utter chaos in the taxi industry. The next wave of displacement will come when autonomous cars make their way not only to the taxi industry but anyone who drives a vehicle for a living, including bus drivers, delivery drivers, ride-hailing drivers, etc. According to a report by Goldman Sachs, drivers could lose 25,000 jobs a month, or 300,000 per year. Truck drivers will see the bulk of that loss according to the report. Below is an image showing the most common job in each state from 1978 – 2014. It is very apparent that “truck driver” takes the cake. The upside of this argument is that there will be less fatal accidents caused by sleep deprivation and lack of focus behind the wheel. Also, the distribution will become far cheaper when you cut out the cost of paying the driver, having an estimated annual savings of $18 billion in transportation. However, this massive wave of job displacement will weigh heavily on local economies as many people will be out of work. This will most likely require some sort of government intervention to provide a livable source of income for these people who will be displaced.  










What roadblocks lie ahead?

Government regulation along with infrastructure are the two main components that will be required for a smooth rollout of autonomous cars. The cameras and sensors fitted onto these literal smart cars can only do so much. Proper road markings and signs are absolutely necessary for optimal functionality. Variations in weather (e.g. snow, heavy rain) can also make these signs and markings harder to see which is also being handled. Government regulations in Sweden have been very friendly towards the auto giant Volvo, which has allowed them to excel in the area of autonomous driving. Sweden also has much higher quality roads compared to the United States, but then again, it’s like comparing Apples to Oranges. Sweden only has a fraction of road miles compared to the 4,071,000 miles of road in the United States, which only 65.7% are paved; this is another challenge for autonomous driving. The autonomous driving industry does face many challenges, but the economic incentives are there to take these challenges head on. Self-driving cars would allow for further productivity (e.g. you can do more when you are not driving) and also will have mental health benefits. There is little to no doubt in my mind this industry will be here before we know it.

 

 

Ian U
Ian U
My name is Ian Unsworth and I am a Junior at Georgia State University studying both Finance & Computer Information Systems. My passion for financial markets began after the 2008 financial crisis when I became intrigued by the massive failure of large financial institutions which for the most part, no one saw coming. The inevitable technological shift of the financial industry has also motivated me to become more educated in areas such as computer programming and data analytics. Any feedback on my articles is appreciated.

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