Autonomous vehicles are no longer a thing of the future. The technology is here and getting better every day. The current systems are however typically bound to specific controlled geographical areas, limiting their usability. There is still a lot of work and research to be done to make a truly independent autonomous vehicle. Advancements in deep learning have increased the validity of using end-to-end systems as a promising alternative approach to current systems.
This thesis explores some of the possibilities of these end-to-end systems and compares the performance of different architectures and techniques i.a. the importance of using temporal data, the importance of the quality of the dataset, classification vs. regression and the effect of increasing the complexity of the system.
This work also explores the implementation of these architectures on the JetBot robotic test platform for the task of both lane following and following navigational directions in a simplified urban environment.
The architecture proposed by Aasbø and Haavaldsen, “Autonomous Vehicle Control: End-to-end Learning in Simulated Environments. is used as a basis. The idea is explored further by applying the findings on the jetbot platform, performing further tests and validating results.
The findings in this thesis show that even a simple deep neural net can achieve full autonomy, given a sufficiently large dataset of high quality. The results with more complex models on the JetBot platform were not promising, with the vehicle regularly ignoring commands or swerving out of lane. Further experiments hinted that this is probably because those models were over-qualified for the simple environment as well as the use of a (too) limited dataset.