Yup that’s a pretty strong title. Odds are it is extreme or exaggerated hyperbole meant to get your attention. Soon replaced by some sort of bait and switch and then the sales pitch starts. NONE of that is true here. As a matter of fact, as I have stated in an article previous to this, autonomous vehicles will NEVER be created if the status quo of using public shadow driving for AI, engineering and test remains. It will never be reached because that process will take so much time and money and literally cause so many fatalities that a multitude of forces will shut the industry down. That includes the public, governments, attorneys, shareholders etc. The belief that that public shadow driving is the best or only way to build this technology is wrong. It is untenable, unworkable and undoable. Worst of all you will lose everyone’s trust, belief that you know what you are doing and as such support and funding.
(Shadow driving is where the driver allows the vehicle to drive. Whether it is to train the system through AI or allowing it the system to control the major features of the vehicle).
What is the solution?
Aerospace level simulation for AI, engineering and test. (Before you leap to the usual conclusion simulation cannot do this let me ask you — have you ever seen aerospace or FAA Level D simulators and simulations? If you haven’t do your due diligence. Then tell me I am wrong and why).
Issues with Public Shadow Driving AI
1. Miles and Cost — One Trillion Miles and $300B
a. Toyota and RAND have stated that in order to get to levels 4 and 5 one trillion miles will have to be driven. This to accommodate the uncontrollable nature of driving in the real world, literally stumbling and then having to restumble on scenarios to train the AI. To accomplish this in 10 years it will cost over $300B. That extremely conservative figure is the cost of 684k drivers, driving 228k vehicles 24/7. This expense in time and money is per company and vehicle.
2. Injuries/Casualties of Public Shadow Driving
a. Data from NASA, Clemson University, Waymo, Chris Urmson (Aurora) and the UK have shown situation awareness and reaction times are very poor. Between 17 and 24 seconds are needed to properly acclimate and react. This delay results in drivers not being able to function properly especially in critical scenarios. They often make the wrong decision or over react. Many including Waymo, Volvo, Ford and Chris Urmson (Aurora) have called for L3 to be skipped due to these issues. The fact of the matter is if L3 is dangerous then so is using public shadow driving for L4 and L5. (The Netherlands uses the simulation and test tracks as opposed to public shadow driving).
3. Injuries and Casualties caused by Complex, Dangerous and Accident Scenarios
a. In order for AI to learn how to handle complex, dangerous and actual accident scenarios it has to run them over and over. And they have to precisely match, or closely match, the original scenario. To date this is not being done. Which is why there have not been a lot of accidents, injuries or casualties. When that time comes the shadow drivers will have to drive and redrive scenarios that include progressively higher levels of complexity, involving many other vehicles or entities, bad weather, bad roads conditions, system errors etc. Many of those scenarios will be known accident scenarios. To learn these situations it will literally mean billions of miles have to be driven and possibly millions of iterations of these scenarios run to get this data. That will result in accidents, injuries and even casualties in the majority of these cases.
b. To date there have been no children or families harmed by using this process. (There have however been injuries and casualties involving drivers). That is largely because only benign scenarios are being run. The public shadow driving be utilized now occurs on well-marked, well lit, low complexity, well mapped and good environmental conditions. Given every company bringing this technology to market would have to drive that trillion miles and learn from progressively more dangerous scenarios, casualties are inevitable. I suggest that when this is known or that first mass tragedy or death of a child has occurred the public, litigators and governments will react strongly. That will halt progress for a very long time. Far more than self-realization and policing would.
4. AI — Machine Learning — Neural Networks have Inherent Flaws.
a. MIT has stated that these processes miss corner or edge cases. They can also be fooled by noise. Which result in spontaneous and unexpected errors. And the engineers using the practice do not entirely know how it works.
If you look at these areas individually, let alone in combination, you can see for legal, morale, ethical and financial reasons public shadow driving is untenable.
As for simulation being the solution. I believe the answer is to create an international Simulation Trade Study/Exhibit and Association . The purpose being to:
1. Make the industry aware of what simulation can do. Especially in other industries such as aerospace. (Where the FAA has had detailed testing to assess simulation and simulator fidelity levels for decades.)
2. Make the industry aware of the MCity approach to finding the most efficient set of scenarios. Bring that one trillion miles down by 99.9%
3. Make the industry aware of who all the simulation and simulator organizations are.
4. Evaluate the available products to determine their current capabilities.
5. Determine how close the industry and any individual product is to filling all the capabilities required to eliminate public shadow driving. Where there are gaps determine a way forward to improving products or possibly creating a consortium. This may involve utilizing expertise from other industries.
6. Note — Most companies use simulation. The issue is to what degree they use it. Most of the individuals and companies in this space are unaware of where aerospace simulation is and that technology can be used to improve the autonomous industry simulation and almost eliminate public shadow driving.
I am aware the language above is strong and direct. The fact is this is a massive echo chamber. An echo chamber built be extremely bright, well-meaning folks in Commercial IT who have built a lot of great things. But — they lack domain experience here and as a result don’t know what they don’t know and are making significant mistakes. That can only be dealt with by a massive intervention. A sonic boom. Before the normal reflex actions kick in and you start pushing back do your homework with an open mind, read what I have written and check my references for the information cited in my first link. As for what is in this for me. What’s my angle? I am in the process of creating a the nonprofit association and trade study/exhibit I mentioned. SAE is assisting me and without a mass invite going out yet I have 7 simulation companies signed up. So yes at some point I could personally gain from this. If you Google my name you will see I was involved in a situation where I tried to change minds in the defense industry and DHS 10 years ago. That resulted in some hard times for me and my family. My point being — doing the right thing matters to me.
For much more detail on this as well as references for what we have stated please see the links for several articles I have written on the subject below.
Due Diligence Recommendations for the Mobile, Autonomous and Driverless Industry
Stop relying on AI to make Autonomous Vehicles — You are wasting time and risking lives
Who will get to Autonomous Level 5 First and Why.