Distributed autonomous intersection management with neuro-evolution

Master Thesis


Permanent link to this Item
Journal Title
Link to Journal
Journal ISSN
Volume Title
The sudden surge in computational power available to computer research in industry and academia has led to developments in AI automation. More and more tasks are able to be automated and replaced with machine learning systems. One such task that promises to be highly beneficial is that of driving, clearly indicated by the amount of resources being spent by companies such as Uber, Google and Tesla. Neuro-Evolution has shown promise in the field of controller development, due to its ability to develop complex behaviour without a need for any labelled training data. It has been applied previously in car controller generation, across many fields. This thesis aims to apply Neuro-Evolution specifically to the field of intersection management, in order to study which methods are the most effective for this particular task. In particular we investigate three key hyper-parameters: Neuro-Evolution algorithm, task difficulty and problem exposure. A traffic simulator was developed and the hyper-parameters were used to evolve car controllers, which where then tested on unseen tasks. We show that certain key combinations of hyper-parameters yield exceptional results, but that direct correlations between individual parameters and performance are unclear, indicating that these methods are highly sensitive to hyper-parameter selection. We further identify some areas in which to optimize the evolution method, by looking at hyper-parameters which have a computational cost but which did not produce better performance.