Starting from the code sent to me last week about animations, I was able to display all the predictions my steering model was making. With just a little bit of training (10 epochs on 10000 data points), it actually performed fairly well. And it looks cool to boot.

Building an acceleration model wasn’t too bad; it’s almost identical in structure to the steering model, except for a final layer difference. Since we can only control the acceleration as much as gas,brake, and reverse, I decided to switch to a categorical model. In truth, I should account for the ordering of these; that is, I care that brake is closer to gas than reverse. If the model’s going to be wrong, I’d prefer that it’s only a little wrong. In classical statistics, you can handle this with an Ordered Probit Model. In layman’s terms, you can think of this as doing linear regression, plus estimating the thresholds between classes. It’s statistically responsible.

Acknowledging that, I moved forward with a standard logistic regression on the bottom of my neural network. This is partially because I chose the thresholds between classes myself, and arbitrarily at that. But, the resulting model was almost always predicting “drive forward”, likely because that’s the overwhelmingly common (about 90% of all times) state. That’s the next obstacle.