…and what they are doing instead.
I originally published a version of this post in June 2016. This update, with the recent news, further confirms some of the assumptions I made back then.
The tweet above confirms something we’re all quite aware of, which is that there is still a lot to do before we get self driving cars onto our roads. But why is this the case? Aren’t these companies the ones with the technical know how and unlimited resources required to make our self-driving car fantasies reality? Well, it turns out there are three technical issues making this difficult.
Barring a horrible spate of autopilot accidents the self-driving car movement will continue in full swing. The technology is improving at pace with Elon Musk declaring that “worldwide regulatory approval will require something on the order of 6 billion miles (10 billion km). Current fleet learning is happening at just over 3 million miles (5 million km) per day.” Every single one of the big technology and automobile players is plowing money and brainpower into being the company that ushers us into this utopian future where there will be zero vehicular accidents caused by human behind the wheel errors. But making this happen is proving more difficult than the companies expected.
My first question here is why did these software first companies assume that they could just turn around and build cars? And why have we all assumed all that matters in this push is the actual development of the hardware/self-driving car? We forget there are a lot of aspects to the technology that is required to make that future happen. It’s never made sense that these companies would build cars.
The Tech We Now Have
There have been some mind-blowing advances in the component technologies required to build a self-driving car.
- Real-time modeling at scale: Carcraft, is Waymo’s World of Warcraft-like software that enables 25,000 autonomous driving simulations in any of the cities where Waymo/Google is currently deploying self-driving cars. This level of testing was not possible before now. Millions of miles of simulation get us to Level 4 (full autonomy), at least in simulations, and ever closer to where we need this to be before it can be unleashed on roads as commercially viable cars.
- Cheaper LiDar: That funny looking sensor that you see on most self-driving cars (image above) is called LiDar, which stands for Light Detection and Ranging. It does exactly what it says in the name and is the key data gathering component for self-driving cars. Commercial LiDar systems went for anywhere between $1000-$70,000, a prohibitive cost for any car manufacturer to add on their vehicles. MIT’s Photonic Microsystem Group is claiming they can manufacture LiDar for as low as $10 shrinking it to less than the size of a coin and Velodyne is claiming they can get a subsystem cost down to below $50! Moore’s Law in full effect.
- Data collection and analysis at Machine Learning scale: Every new Tesla currently driving thousands of the miles on the road is gaining knowledge of how to autonomously navigate our human infested roads. With every additional mile driven by each car, more collective knowledge is gained and dumped in a central database where it is analyzed in real-time using machine learning. Tesla’s swarm of cars is enabling even more simulations of what driving in real life looks like. Instead of writing rules for the vehicle to follow, the system can use ML to make decisions based on the reams of data that it has been trained on.
What We Still Need To Figure Out.
- Edge cases that are actually the norm (Brains): Training a self driving car is possible in the US because there are enforceable and encoded rules and regulations that govern the road. While these rules might be broken, leading to accidents etc, for the most part we are conditioned to follow the rules of the road. And you can train your software to follow those rules. But this is not the case in places like Nigeria where there are no rules or consequences for breaking the few rules that exist; you need huge numbers of simulation to cater to the possibilities. The simulation requirements are further complicated by the lack of structured roads; paths become roads and fall back into being paths before you’ve had time to update your database/Carcraft.
- Technological challenges (Brains and Body): continuing the point above, the technological complexity of a world without rules is one that will test the limits of self-driving car capabilities. Redundant mechanical systems have to be built into the cars to ensure that the car responds adequately when these edge-but-normal cases happen. It is not about the cars, per se, it’s about the rest of us. We are truly unpredictable . Technology is not advanced enough to capture the context and mind state of a driver in another car. The time between now and when all the other cars on the road are self-driving will be an interesting one.
- Hardware is still pretty hard: The production and roll out delays of Tesla cars (and the death of many software enabled hardware companies) lead us to forget that launching hardware is hard. We forget the technological difficulties of putting together a vehicle and, hurriedly, rush into assuming that combining advanced software into fast changing materials hardware is a breeze. Hardware is still hard.
So what is the best strategy for a tech/software company that hasn’t traditionally built cars?
Apple Case Study
Like Microsoft/Cortana for Nissan and BMW and Waymo/Google with Honda the best approach for a software DNA company is to build the operating system for autonomous cars. And it’s what Apple is doing. Apple is building the autonomous car Operating System (OS) based on the iPhone/Apple Watch as the original portable telematic device: knowing where a car is, what condition it’s in and recording that information to make better decisions is something that will be required in every autonomous car. We all know our iPhones are already personal telematic devices (I hope you do). The phone tracks your location and your health (see images below). There are two elements to autonomous driving software, maps and driving data. Building a CarOS, for Apple, would be a natural extension of the maps and health software that is currently being captured as you and I navigate the world.
Becoming the CarOS is the most strategically adjacent move Apple can make. Why would a company ignore it’s most sold asset, surpassing 1Bn iPhones sold this past week, to focus on a product that is capital intensive and not its core competence? Why would Apple enter a space where the most talked about company in the space is one that has not delivered more than 250k cars? Yes, the orders for the Tesla Model 3 has broken records for EVs sold but the real work will be in delivering those cars and that will be interesting to watch.
As we move into a future where fewer people will actually own cars but people will continue to buy iPhones I do not expect Apple to move away from what is working for it.
Imagine an OS that enables any brand of autonomous car to work with the telematic system that is on the riders iPhone? Seamlessly.
A standard autonomous CarOS on your iPhone will improve routing and getting a car to you regardless of the cab/car service you choose to use — the autonomous Uber cab app will most likely only work with Uber cars — and improve customer service since you’ll get the closest available car/cab regardless of the autonomous car service provider.
Apple is learning from the best engineers in this new industry so that it can do to the industry exactly what it did to the music industry with iTunes. An CarOS will iTunes the industry by
- enabling each one of us with an iPhone to order any car or cab from any provider where you need it from your iPhone. It’s the a la carte model like the 99c single from ITunes.
- levelling the playing field and accelerate the shared resource model where anyone with an autonomous car can rent out their own car to anyone without an intermediary and get paid through (you guessed it) Apple Pay.
- combining with Apple HomeKit to provide a seamless convenience experience across the home and vehicle of the Apple customer unmatchable by no one else in the industry with their siloes of service.
If we accept that Apple cares about design and understands the customer then an autonomous car, with all it’s inherent business risks, is the wrong strategy. Something else I know about Apple is that the company is too smart to continue to cede leadership in the battle for the future. A future being defined by Amazon (Alexa), Alphabet and Facebook (Oculus etc). A CarOS for the autonomous future of the vehicle and (the connected home) makes so much sense for Apple. It’s the only low-investment, do-what-you’re-good-at strategy Apple, and the other big tech companies, can take. The technology for Autonomy will become a commodity. Key question for the tech companies is, who will get there first with a robust enough operating system that the car companies will have to depend on?
Conclusion: I believe we will end up 3–4 platforms/OS and the traditional car manufacturers will select which autonomous software provider they will partner with. And it’s another compelling reason why Apple might buy Tesla…
You might like this other article on Apple
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