I would guess that mapping ”nothing here” to a value that is higher than the others would give better results. The software wouldn’t have to learn that weird inversion where the safest value is very close to the least safe ones.
The mental model is like this: if sensor says 4 - it means the obstacle is 4 meters away. If obstacle is far away, then sensor may say… hm… 5 meters? 10 meters? Infinity meters? So I went with something a bit higher than max sensor distance limit of 4 meters. And, for linear equation this didn’t work for me. Cars were straggling to learn.
So I’ve switched to another mental model: if sensors says 0 - it means we just turn the sensor of, the sensor is not important. Let’s say you want to learn how to drive forward if the obstacle is behind you. Then you don’t care about the side sensors, you may just cancel them with zero variables. And with this setup, the cars started to learn much faster.
I think the correct approach depends on the brain “model”. For linear equation, canceling the sensor with the zero value of the sensor.
But if you would manage to train the cars well with the different approach - it would be really interesting to try
That also makes sense if you interpret “sensor says 3” not as “obstacle is 3 meters away”, but as “there’s 3 meters of room”.
But then, I didn’t try to see what works better. I find the result surprising, though.
nn_input = 2/(1+exp(-distance))-1
This also captures the fact that differences in small distances are more meaningful.