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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).

enter image description here

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.

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  • $\begingroup$ You could try a different approach. Your problem seems to be the same as the ones with autonomous cars. Why not use an embedded camera, real-time image recognition like Yolo, and a Reinforcement Learning process? $\endgroup$
    – Nicolas Martin
    Jun 7 at 6:25

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