I'm using a model car which can be remotely controlled and which has a front facing camera.
My goal is to train a CNN which would be able to efficiently park this car in a simple environment (pictured below).
During the training process I would put the car in a random spot in my room (with the blue box visible on the camera) and I would park the car in the spot with the blue box (I'd switch the box every 10 or so parking "episodes"). While driving I save an image alongside the steering value ([-1,1]) and throttle value ([-1,1]) at a rate of 10 images (frames) per second. Training set consists of 10_000 images and the validation set consists of 1000 images.
Using this approach I tried training several different CNNs (mine, ResNet18, ResNet34, ResNet50...) but all the results were fairly disappointing. The only thing I can say is that the car learned that it should stop when it gets close enough to the blue box.
This leads me to believe there's a fault somewhere in my approach. I would be grateful if someone would be willing to share any tips or even better if someone has some experience with the goal I'm trying to achieve.