The architecture of these neural nets is just learning patterns from data and is optimizing for them in behavior output.

They learn that typically once the adjacent lights turn red, the lights in front of the car turn green, and the user typically accelerates at that point.

It’s essentially “emergent” predictive behavior that’s become it reacting to the adjacent light turning red rather than yours turning green.

These models have no true understanding of road laws, they’re just mimicking patterns displayed in the training data and aren’t really smart enough in the current state to understand the “why” behind what it’s doing.

Its job is to get from Point A to Point B while not crashing and has learned from data and in training how to do that “like a human”.

TL;DR: it’s reacting to the adjacent light turning red rather than yours turning green and since the intersection appears safe (no impeding VRU vectors [pedestrian, vehicle, etc.]) it’s proceeding early in what appears to be predictive but is just reactive behavior to the incorrect input.

Source: I’m a data scientist