GM Cruise – Sim city

I am not a big believer in simulation.

• GM Cruise is one of the leaders in autonomous driving, but I suspect that its use of simulation as opposed to the real world will prevent its performance from getting to a level where it can be truly called autonomous.
• GM Cruise held an event last week where it discussed its autonomous driving technology in detail with a focus on two areas in particular.
  • First, custom silicon: Like Tesla and NVIDIA, Cruise has designed its own silicon that it intends to include in its vehicles that will execute the autonomous driving.
  • This makes sense because one of the biggest hurdles to the adoption of autonomous driving (excluding technology) will be the cost of deploying it into vehicles.
  • Thanks to the cost of lidar and compute, autonomous driving is both very expensive to deploy as well as a significant drain on resources.
  • In EVs, this is obviously a problem as it will sap the vehicle’s range which remains one of the key differentiators from one proposition to the next.
  • There is not that much that Cruise can do about lidar but following Tesla down the custom silicon route makes a lot of sense.
  • Second, simulation: GM Cruise is increasingly using simulation rather than real-world driving to iron out the kinks in its autonomous driving software.
  • This is done by mapping a real-world city and then recreating that city within a simulation so that the software’s ability to navigate that city can be tested and refined.
  • I consider the biggest problem with autonomous driving not to be the decision-making capacity of the system, but the perception of the environment upon which those decisions are based.
  • Hence, the biggest problem to solve is the interpretation of the real world accurately such that the machine can make good decisions.
  • Almost all of the horrible mistakes I have seen Tesla and everyone else make are not based on decision making but on the incorrect perception of reality.
  • Hence, when one recreates reality in simulation, the task becomes easier as much of the perception that the system must do has already been done for it.
  • Furthermore, simulations are limited to the rules upon which they have been based and therefore may not be an accurate representation of reality.
  • This is why I am pretty sceptical about the ability of simulation to produce an algorithm that can drive reliably and safely in the real world.
  • There are uses for simulation in the training of algorithms, but I think it will struggle to deal with the endless corner cases that keep tripping all of these systems up.
  • I have long believed (see here) that the solution that is most likely to work will be one that combines deep learning systems and software.
  • This is because deep learning can learn but not reason and software can reason but not learn.
  • Hence, combining the two should be complementary and help offset each other’s weaknesses.
• Despite both Cruise and Waymo receiving licences to operate driverless vehicles in California from the DMV, they also need a licence from the California Public Utilities Commission (CPUC) and the time scale on this is completely unknown.
• Hence, I think that we are still pretty far away from true driverless vehicles although there will continue to be vanity projects launched here and there.
• I do not see anything that would lead me to change my estimate of 2028 before autonomous driving is properly commercialised meaning that many start-ups are going to run out of money.
• There has already been considerable consolidation and I expect more to come.