Self-Driving Cars Part 3: Key Challenges, Players, State of the Market Right Now

As mentioned in the first part of this three-part series on self-driving  cars (see the links below), the two distinct models for ownership of autonomous vehicles comprise: 1) self-driving cars that are be owned by large companies and operated like an on-demand car rental service; or 2) self-driving cars are owned by individuals and run like the Uber or Airbnb model. It could well turn out that ownership models span the spectrum with various large and small corporations owning vehicles in addition to individual owners. All companies looking to cement their position in the new ecosystem will have to master the three technology areas: operating system for self-driving, routing technology and mapping services.

Operating System for Self-Driving

The key players that have emerged as forerunners in this domain are Google (NASDAQ:GOOG), Tesla Motors (NYSE:TSLA) and Uber, along with some other players such as Nutonomy in Singapore and EasyMile buses in Finland. There are different schools of thought on how to best solve the learning problem for a self-driving car. For instance, Google developed their own self-driving car and released it on the road. According to latest available data, the Google car has driven more than 1.8 million miles on public roads. This exposure and testing condition allowed it to build a reference data for situations it might encounter in the real world and develop rules to best negotiate those situations. ^[1] In contrast, Tesla allowed its system to learn from collected data from human inputs while driving and compiling it in its system. ^[2] Uber is also running a driver-assisted pilot in Pittsburg, PA with a 100 Volvo plug-in hybrid SUVs ^[3] Uber has further acquired the self-driving trucks startup Otto for $680 million

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Mapping

Mapping is an integral part of the self-driving functionality with many tech companies and automakers investing heavily in obtaining precise mapping capabilities. Uber acquired assets of Microsoft’s (NASDAQ:MSFT) Bing maps last year to bolster its mapping requirements. ^[4] German automakers BMW, Daimler and Audi jointly acquired Nokia’s Here digital maps business for $3.1 billion last year to help them independently develop self-driving cars. ^[5] Google and Baidu (NASDAQ:BIDU) have an advantage in this space given that they already have their own mapping applications. Baidu partnered with graphics company Nvidia (NASDAQ:NVDA) on a “cloud to car” mapping system for self-driving cars. ^[6]

Routing Technology

Solving the routing problem is the third key element to crack the TaaS market. Uber developed its complex algorithm when it launched UberPool in various markets to best cater to users. Finding the most efficient way to provide cars is essentially a more complicated variant of the classic traveling salesman problem. ^[7] As a result Uber has the first movers advantage in this space. Moreover, Uber already has a huge pool of drivers and riders giving it the edge over competitors in this space. Developing the entire back-end infrastructure and expertise in directing cars to travel or position in a certain direction should make it a capital-intensive venture for new players looking to enter the TaaS market.

This was presumably the key reason why Google acquired Israeli ride-sharing company Waze for $1.1 billion last year. ^[8] Similarly, other auto companies such as Ford Motors (NYSE:F), General Motors (NYSE: GM) and Toyota Motor (NYSE: TM) are partnering with or acquiring ride sharing competitors of Uber to become familiar with the complexities involved in ride sharing operations. GM announced the launch of Project Maven earlier this year to gain experience in the ride-sharing operation. ^[9] Ford partnered with Uber by providing its Ford Fusion cars in the launch of the pilot ride-sharing project for self-driving vehicles. Further, Volkswagen made a $300 million investment in the Uber rival Gett, in a move towards having its own ride-sharing autonomous vehicles. ^[10]

Given that all three sub-segments revolve around gathering data, learning from data and developing systems to deal with real life problems, it gives an edge to software companies at the moment. If one considers the end-to-end supply chain of the products and services involved, the most valuable component here is software and machine-learning capabilities. Auto companies have an urgent need to level up their software capabilities, failing which they could remain as hardware vendors or OEM providers in this business in the long run.

Read more on this topic:

• Self-Driving Cars: The Building Blocks of Transportation-as-a-Service
• Self-Driving Cars Part 2: Size of Opportunity Involved

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1. Google’s Self-Driving Car Project Is Losing Out to Rivals, Bloomberg, September 2016 [↩]
2. Why Google Should Be Very Afraid Of Tesla Autopilot, Forbes, October 2015 [↩]
3. Uber and Volvo to develop self-driving cars, Finacial Times, August 2016 [↩]
4. Uber acquires Bing Maps technology, employees in Microsoft deal, Mashable, June 2015 [↩]
5. BMW, Daimler and Audi Clinch Purchase of Nokia’s Maps Business, Wall Street Journal, August 2015 [↩]
6. Nvidia Teams Up With Baidu On ‘Cloud-to-Car’ Map System For Autonomous Vehicles, Forbes, September 2016 [↩]
7. Google, Uber, and the Evolution of Transportation-as-a-Service, Stratchery, August 2016 [↩]
8. Waze founder tells us how its $1 billion sale to Google really went down, Business Insider, August 2015 [↩]
9. How GM’s Maven car sharing service got to over 4.2M miles driven in 7 months, TechCrunch, August 2016 [↩]
10. VW invests $300M in Uber rival Gett in new ride-sharing partnership, TechCrunch, May 2016 [↩]