1. How to use GuruFocus - Tutorials
  2. What Is in the GuruFocus Premium Membership?
  3. A DIY Guide on How to Invest Using Guru Strategies
John Engle
John Engle
Articles (406) 

Tesla Has No Lead in Autonomous Vehicles

While Elon Musk has convinced the public of his genius, the experts are not so sure

November 30, 2018 | About:

Tesla Inc. (NASDAQ:TSLA) loves to talk about autonomous driving. As we discussed in a recent research note, CEO Elon Musk has been beating the drum about the imminent arrival of full self-driving, or FSD, capabilities for years.

Of course, Tesla tends to be long on promises and short on delivery, and that has been especially egregious when it comes to full self-driving. But it is more than a matter of the company promising the moon and failing to deliver. After all, that is a tried-and-true Tesla tactic.

This is what makes Tesla’s approach to full self-driving a special case even when set beside its other big promises of future technology: Despite making public claims to leadership in autonomous driving technology, the company is actually way behind.

Falling way behind

While Musk has worked hard to cultivate the image of Tesla as the undisputed leader of autonomous driving, this is simply not the case. Indeed, all evidence points to Waymo, a subsidiary of Alphabet Inc. (NASDAQ:GOOG), and General Motors (NYSE:GM) as the clear leaders in the field. In fact, given Tesla’s dearth of patents, lack of strong strategic partners and general absence of booked testing miles, one might well conclude that the company is not even in contention.

Autopilot, Tesla’s driver assist system, is not even considered best-in-class for what it does. GM’s Super Cruise takes the cake on that score, according to Consumer Reports’ ranking of automated driving systems. Meanwhile, a study by Navigant now ranks Tesla dead last in autonomy.

And Tesla has little hope of catching up with Waymo and GM. It will likely even languish behind the likes of Ford (NYSE:F), which is making a valiant entry into the technology and investing heavily to close the distance with the leaders. Investment is the key phrase in this race.

Tesla has miniscule financial resources by comparison to the other automakers and big tech companies, and it is almost certainly going to have to turn to capital markets simply to fund its next line of vehicles. It has extremely limited resources to invest in the autonomous driving race. Meanwhile, others are pouring billions of dollars into research and development.

Neural nets are not the answer

Despite the mounting evidence to the contrary, Tesla behaves as if its technology is miles ahead. The public has bought into this idea thanks to a mostly credulous media, but the experts are not so easily swayed.

Tesla bulls argue the company is actually ahead because it is playing a whole different game. Specifically, they allude to the use of neural nets to master image recognition and capture data in a way other players in autonomy cannot. There is one problem with this reasoning, however, and that is that neural nets are simply not up to the task, as Josh Sacks, an expert in the field, recently explained in a blog post:

“Advances in Deep Learning have made various image processing and recognition tasks possible. Smartphones now do extensive computational photography using neural nets. Nest cams use neural nets to identify people, as do Facebook and other social networks.

These tools generally work well. However, they don't work 100% of the time. In fact, they are not that close to 100%. If Portrait mode fails once every fifty photos, who cares? If Facebook suggests the wrong person in a photo, does it really matter? These tools are commercially viable because they mostly work, and 98% accuracy is far more than needed for most use cases.

“It's possible that NN's are incredible and great for a ton of applications, but also not nearly good enough for driving safety-related decisions. Software that's right 99% of the time isn't good enough when deciding if a biker is in your lane.

98% isn't nearly enough for autonomous vehicles.

In fact, 99.9% isn't good enough. I'd hazard that AV safety engineers probably want 5 or more 9's of reliability- 99.999%. That might seem excessive, but even that allows for a 1:100,000 chance of misidentifying a pedestrian in your path. Given what we know about existing solutions and the difficulty of the problem, it's unlikely Tesla's NNs even get to 99.5% error rates for many safety critical classification tasks. 99.5% would be a major achievement. To use a phrase familiar to Tesla fans, Tesla is orders-of-magnitude away from a viable FSD solution. Then need their system to be at least 100x more accurate.

This is why every other company pursuing AVs is using lidar. Lidar is extremely reliable and accurate. If your lidar sensor says there's nothing in your path, then there's nothing in your path, especially when you have two independent sensors looking in the direction of travel. That's what's needed to get to 99.999% reliability. For all the talk about NN advances, the fact of the matter is that error rates for critical decisions are still way too high.

No one in the field has any idea how to lower those error rates another 10x, let alone 100x. It's going to take a major breakthrough (perhaps more than one) to get visions systems reliable enough to depend on for driving.

So next time someone starts talking about using neural nets for FSD, ask if they think those systems can get to 99.999% accuracy. Ask if anyone anywhere has every demonstrated a vision system on a real-world task this accurate.”

In other words, Tesla is promising a level of functionality in its neural nets technology that is not possible now, nor is likely to become possible anytime soon. Meanwhile, serious players in autonomous driving are using safer, proven technologies that utilize overlapping sensors to prevent the sorts of blind spots that will inevitably afflict even a superlative neural net.


In essence, the whole Tesla autonomous driving project is flawed in its conception. Its reliance on a brittle technology, one that is years (if not decades) from being sufficiently robust to guide an full self-drive system, makes it a laggard in the field. And, while other well-heeled players pour billions of dollars into their technologies, Tesla has been cutting back on growth capital expenditures and is guiding for shockingly low levels next year.

Tesla has no edge in full self-driving, or autonomous driving of any kind. Its one advantage is a public image that has been carefully cultivated, combined with a marketing protocol that is willing to sell technology that does not exist (and may never really exist) to existing customers. That is bad business, and Tesla will ultimately pay for its mistakes.

Disclosure: Short TSLA via long-dated put options.

Read more here:

About the author:

John Engle
John Engle is president of Almington Capital - Merchant Bankers. John specializes in value and special situation strategies. He holds a bachelor's degree in economics from Trinity College Dublin and an MBA from the University of Oxford.

Rating: 2.3/5 (3 votes)



Bwilson4web - 10 months ago    Report SPAM

There have been two, drunk drivers who did not crash while using Tesla's AutoPilot:

Documented saves are a strong argument for Tesla technology.

Please leave your comment:

Performances of the stocks mentioned by John Engle

User Generated Screeners

pascal.van.garsseHigh FCF-M2
kosalmmuseBest one1
DBrizanall 2019Feb26
kosalmmuseBest one
DBrizanall 2019Feb25
MsDale*52-Week Low
Get WordPress Plugins for easy affiliate links on Stock Tickers and Guru Names | Earn affiliate commissions by embedding GuruFocus Charts
GuruFocus Affiliate Program: Earn up to $400 per referral. ( Learn More)