The real way is to use the LIDAR to add to the depth probability distribution in the stereo depth estimation. This way you aren't throwing any data away. LIDAR often gives probabilities as well, for example, and this can be used to eliminate reflections.
Reading sensor data is not the same as feeding that data to a neural network and asking it to form a worldview composed of possibly conflicting sensor data streams(i.e. lidar vs vision vs ultrasonic).
You are somewhat correct that it is quite trivial to read sensor data. For many sensors, there is some work which needs to be done to denoise or cleanup the input data. That's not where the story ends, however.
Sensor fusion is computationally cheap. It's just a lot of R&D to do it in a way that leads to a net gain in precision and robustness.
So it seems like a totally ridiculous argument that ultrasonic sensors create some kind of data processing overload.
An ultrasonic sensor makes it possible to implement incredibly simple and reliable safety features with well known performance characteristics. Processing an image with ML to produce the same effect has tons of edge cases where it might not work, and nobody knows when it won't work, and every update to the system could introduce regressions.
It's why they had to disable certain features when they got rid of the ultrasonic sensors. Those features may come back some day, but I bet they'll never be as reliable, and certainly won't be as predictable.
https://www.pcmag.com/news/tesla-removes-ultrasonic-sensors-...