The problem Self-driving Car Simulation solves

The goals/steps of this project are the following:

- Use the simulator to collect data on good driving behavior
- Build a convolution neural network in Keras that predicts steering angles from images
- Train and validate the model with a training and validation set
- Test that the model successfully drives around track one without leaving the road

Challenges we ran into

Though the models performed well for the track it was trained with, the real challenge was to generalize this behavior on a second track available on the simulator. The dataset for Track_1, which was simple with favorable road conditions to drive, was used as the training set to drive the car autonomously on Track_2 which consists of sharp turns, barriers, elevations and shadows. To tackle this problem, image processing and different augmentation techniques were used, which allowed for extracting as much information and features in the data as possible. Ultimately, the car was able to run on Track_2 generalizing well. The project aims at reaching the same accuracy on real-time data in the future.