Just a comment about starting with fewer or more sensor types.
In machine learning, the goal is to find the data set (sensor configuration and its data) and an associated model architecture (neural network, etc) that is sufficiently predictive to do AI driving.
The universe of data and models is enormous.
How does one search this vast collection of data and models to find the best ones?
This is fundamentally an optimization problem.
This is the challenge of feature selection and model selection.
There is no single approach on how to search the vast collection of features and models, but certain approaches have been developed in machine learning, when applied to any problem (not just AI driving).
One approach is to start with fewer features (eg, fewer sensors and associated data, such as camera only) and then add more to check if there is AI model improvement.
A second approach is to start with more features (eg, more sensor types and associated data, such as camera plus other sensors) and then remove to check if removal doesn’t materially degrade AI model performance.
Tesla says they have already progressed through the step of reducing sensor types, except for much more limited data collection of camera plus lidar.
Waymo and others remain multi-sensor.
Yet, Waymo is way ahead of Tesla when the benchmark is miles of robotaxi fares.