I have made this prediction multiple times throughout the last 8 years based on my knowledge of robotics. I have argued with family members who are VCs as well

I have made this prediction multiple times throughout the last 8 years based on my knowledge of robotics. I have argued with family members who are VCs as well as enthusiastic technical observers based on simple first principles reasoning. It is highly unlikely that completely autonomous vehicles will be present on the streets - interacting with humans, cars, and random events in real-time - within the next decade. What is more feasible and likely to occur is that large chunks of trucking may become "automated" in the form of a hyper-advanced cruise control, but these vehicles will lack Level 5 autonomy for their whole route. To understand why, requires a deeper examination of where this technology came from; Stanley, Thrun’s robotic car that was acquired by Google and is the basis for Waymo’s tech. Thrun notes; > “In the last Grand Challenge, it didn't really matter whether an obstacle was a rock or a bush, because either way you'd just drive around it," says Sebastian Thrun, an associate professor of computer science and electrical engineering. "The current challenge is to move from just sensing the environment to understanding the environment." http://news.stanford.edu/pr/2007/pr-junior-021407.html http://news.stanford.edu/pr/2007/pr-junior-021407.html More precisely, to interact and function in the wild, the robot needs to understand, distinguish, and model the properties of wildly different entities. And these entities aren't just other people - they are everything in its environment. And this includes random events like; people walking into traffic, objects falling onto the road, sudden rain and hail, or just potholes. This is not a problem that can be trivially solved with better sensors or cheaper LIDAR. It is a conceptual problem that can not be trivially brute forced nor simulated in advance as each unique permutation is just different enough to confound existing models. Even if such different and confounding events are six sigma in nature and have a probability of occurring 0.00034% for every mile driven, then at 3.22 trillion miles/year (https://www.npr.org/sections/thetwo-way/2017/02/21/516512439/record-number-of-miles-driven-in-u-s-last-year https://www.npr.org/sections/thetwo-way/2017/02/21/516512439...) Americans will experience 10,948,000 anomalies per year. Or, about 20.8 events per minute. 20.8 anomalies per minute is a pretty big number. That’s a potential accident every 3 seconds or so. And that’s a six sigma estimate. It is hard to emphasize how hard six sigma performance is for the current generation of autonomous vehicle hardware. You might dispute the numbers, but they are meant to be an illustration and an intuitive explanation for why this problem is Hard with a capital H. Unlike some professionals, I don’t believe Strong AI is necessary to solve this problem, but Weak AI is still a form of AI. Technology that's just out of reach today. At this point, people counter with Google’s cars. After all, seeing is believing, and haven’t they been seen to run successfully for millions of miles at this point? Yes, but... Waymo/Google's success is hard to replicate. No one else can bend the rules far enough to achieve their headway. Most of us forget that before starting the project, Google used LIDAR to create highly detailed maps of every environment their cars will ever be in. Engineers then captured standard human driving on that route over and over across different hours. These elements were then combined within their model to pre-compute routes before any real world driving happened. Google was able to achieve its miraculous results because of its ownership of GMaps, Street View and the resources to deploy a fleet to map everything in advance. No one else has these resources. No one else can acquire them cheaply. And even Alphabet, a trillion-ish dollar corporation can't replicate this package for the entire world. Maybe someone will figure out a way to create precise sub-eighth-of-an-inch level 3D models of environments using drones and satellite images, but until that time this technology is too expensive to deploy. It is hard to emphasize how important a distinction this is. The "only" thing a Google/Waymo robot does in real-time is that it checks the difference between the recorded dataset and whatever the real-time LIDAR + camera feed is saying. Or in other words, for every point of the route, Google has generated an expected set of values, and any deviations from these are used by the car to make real-time decisions. https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works https://spectrum.ieee.org/automaton/robotics/artificial-inte... However, even this partial solution is tempting. Because it works. But, sadly, that's not the whole story. What gets lost in the re-telling of this story is that this technique means that the cars can't work in unstructured environments. They are unsafe for most roads, especially uncontrollable, unpredictable and narrow residential roads. It's why these cars exist inside strict geo-fences even when they're ferrying passangers around, like in Phoenix. Where the car seems to mostly serve the downtown area with very little residential coverage (caveats apply, I couldn't find a map). But, even getting this far is a huge technical achievement, even if it's not the fundamental breakthrough we've been looking for. We should be prudent, but optimistic. There are a lot of smart people out there who are working on this problem right now. They are figuring out really clever ways to deal with these edge cases and they will solve this problem. Even if it's not on the hype cycle's schedule. With that in mind, I'd like to suggest a better model for thinking about Driverless Vehicles is another Google service, Translate. It's quite good, but when was the last time someone translated an entire book with it (and it came out to be readable/sensible)? The system's performance - due to its statistical roots - seems to be fundamentally asymptotic w.r.t. the data presented to it. It can't be improved beyond a certain point even if you throw more data at it. I suspect that the current generation of driverless vehicles will be the same. http://people.csail.mit.edu/brooks/papers/elephants.pdf http://people.csail.mit.edu/brooks/papers/elephants.pdf http://arxiv.org/pdf/1604.00289.pdf http://arxiv.org/pdf/1604.00289.pdf Before the needed breakthrough happens, it is likely that the cutting edge won't advance beyond Level 3 in the next decade; > I would guess Tesla’s position on this would be that most of the time, yes, you can rely on it, but because Tesla has no idea when you won’t be able to rely on it, you can’t really rely on it. https://spectrum.ieee.org/cars-that-think/transportation/self-driving/fatal-tesla-autopilot-crash-reminds-us-that-robots-arent-perfect https://spectrum.ieee.org/cars-that-think/transportation/sel... And that's okay. We might not have driverless cars by 2020, but we probably will by the mid 2030's and that sounds pretty darn fantastic to me.