Analysis and Evaluation of Recurrent Neural Networks in Autonomous Vehicles

Abstract: Once upon a time cars were driven by the pure will and sweat of decent humans. Today technology has reached the point in which complex systems can drive the car with little or no human interaction at all. Whilst it does take away the sweet Sunday drive, one has to consider the positives. Over 90% of all vehicle accidents can be credited to the driver. City traffic can be optimised to avoid congestion. Additionally extending the morning nap to the car ride to work is truly something to strive for. One of the way autonomous driving can be achieved is through Artificial Neural Networks. These systems teaches a model how do drive a car through vast and vast amounts of data consisting of the state and the correct action. No manual logic required! One of the many issues these systems face is that the model only analyses the current state and has no inherent memory, just a million small independent decisions. This creates issues in situations like overtaking as it requires a longer plan to safely pass the other vehicle. This thesis investigates utilising the Recurrent Neural Networks which are designed to analyse sequences of states instead of a single one with hopes that this may alleviate the sequential hassles. This is done by modifying an 1/12 scale RC-car by mounting a camera in the front. The images were used to control both steering or velocity in three separate tests which simulates normal driving situations in which the sequence of events contain information. In all three scenarios three different networks were tested. One ordinary single-state model, a model evaluating 5 states and model evaluating 25. Additionally as a ground truth a human drove the same tests. These were qualitatively compared and evaluated. The test results showed that there indeed sometimes were an improvement in utilising recurrent neural networks but additional and more repeatable tests are required to define when and why.