Autonomous Driving – Causality Debate

I don’t think Wayve is going to make it.

• Wayve’s deal with Nissan is a big shot in the arm for the “brute force” approach to autonomous driving, but Nissan has made no promises to use it beyond level 2 leading me to think that a somewhat reluctant Nissan has been coaxed into giving it a try by SoftBank.
• Wayve is a UK-based autonomous driving start-up that uses a single large end-to-end model to drive the vehicle.
• This means that sensor data goes in one end and driving instructions to the vehicle pop out the other.
• The advantage of this is that if one can get to work, then there is no need to limit where the vehicle can go which also means that no HD map will be needed.
• The dream of autonomous driving is to have software that can drive a vehicle more safely than humans under any conditions and be able to deal with situations for which it has not been explicitly trained.
• This is exactly how humans do it and as long as one is prepared to exchange a large neural network for a human brain, then all should be well.
• However, this is a bridge too far for me which brings us right back to RFM Research’s old chestnut of causality.
• Humans can drive a vehicle safely because they understand the cause and effect of the road, while the large model merely matches inputs to statistical characteristics and estimates what the output should be in the given situation.
• For example, any human would never mistake a large restaurant sign with red, yellow and green circles for a traffic light but unless the machine has been explicitly taught about that sign, it will.
• This means that for situations where the dataset is both stable and finite (i.e. all outcomes can be predicted and trained for), then a neural network can perform really well.
• However, the road is neither finite nor stable which makes a large neutral network a suboptimal choice to solve this problem.
• This is where opinion in artificial intelligence diverges.
• On the one hand, you have Elon Musk, OpenAI, SoftBank, Anthropic and so on who claim that with a big enough model, enough data and enough compute, magically, machine superintelligence will pop out at the other end.
• This is the argument that keeps the money pouring in and the valuations at very high levels.
• On the other hand, there are the sceptics and gadflies like Gary Marcus, RFM Research and many others who think that until a statistical-based system can truly reason, we will be as far away from superintelligent machines as we were 10 years ago.
• In my opinion, the “reasoning” models are not actually reasoning but simply offering up a very good simulation of it.
• This is because while the models can ace PhD level maths, they fail to reason that if A=B, then it follows that B=A.
• This is the classic paradox that has plagued AI for decades in that machines can be taught to do very difficult things but fall to bits when asked to do the simple stuff.
• It is not until this issue is beginning to be solved that I think the Wayve approach to autonomous driving has a chance of working in a truly commercial setting.
• One can see this in how Nissan will be using Wayve’s technology starting in 2027 where it will be used for level 2 only at the outset (see here).
• Level 2 is hands-on ADAS where the human is still piloting the vehicle and does not go much beyond staying in lane and adaptive cruise control.
• I take this to signal a “let’s see” approach and I suspect that as SoftBank is a major investor in Wayve and is championing a collaboration between OEMs to share data and resources to achieve full autonomy with an end-to-end system, it has had some influence on Nissan when it came to taking software from Wayve.
• Nissan has made no commitment that I can see to take this beyond level 2, and so I do not take this as a sign that the end-to-end large model approach is the right one.
• In fact, I think this approach will end up falling short and an approach that uses a combination of rules-based software and machine learning will be the one that wins out at the end of the day.
• This also means that autonomous driving components such as an HD map, lidar, radar and cameras will all be needed to help reduce the volatility of the dataset of the road as well as produce redundancy that can make the cars safer than humans.
• With all of the hype and excitement around LLMs, this approach is currently not in favour, and so I suspect that it will be later rather than sooner that we begin to see fully autonomous vehicles on the road.
• Hence, I think that Wayve and Deeproute.ai (which also uses this approach) will never become going concerns in their own right and will end up being acquired.