There is a natural tendency in ML to reach for complexity the moment performance drops. We throw more layers at it, add data augmentation, or stack another ensemble.
But in ADAS decision modeling, some of the highest value moves come from better features but not more of them.
Think about the metrics that actually matter on the road:
-> Time-to-collision
-> Lateral velocity relative to the ego path
-> Lane confidence scores
-> Road curvature
When you encode the domain reasoning into features, decision trees can learn with less depth and more stability.
A shallow tree with well engineered features is almost always more trustworthy in production than a deep tree built on raw sensor noise.
Domain knowledge doesn't compete with ML. It actually makes ML work.
What is the one feature that improved your model more than any architecture change?
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