Abstract
In recent years, considerable progress has been made towards a vehicle’s ability to operate autonomously. An end-to-end approach attempts to achieve autonomous driving using a single, comprehensive software component. Recent breakthroughs in deep learning have significantly increased end-to-end systems’ capabilities, and such systems are now considered a possible alternative to the current state-of-the-art solutions.
This paper examines end-to-end learning for autonomous vehicles in simulated urban environments containing other vehicles, traffic lights, and speed limits. Furthermore, the paper explores end-to-end systems’ ability to execute navigational commands and examines whether improved performance can be achieved by utilizing temporal dependencies between subsequent visual cues.
Two end-to-end architectures are proposed: a traditional Convolutional Neural Network and an extended design combining a Convolutional Neural Network with a recurrent layer. The models are trained using expert driving data from a simulated urban setting, and are evaluated by their driving performance in an unseen simulated environment.
The results of this paper indicate that end-to-end systems can operate autonomously in simple urban environments. Moreover, it is found that the exploitation of temporal information in subsequent images enhances a system’s ability to judge movement and distance.
H. Haavaldsen and M. Aasbø contributed equally to this work.
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Haavaldsen, H., Aasbø, M., Lindseth, F. (2019). Autonomous Vehicle Control: End-to-End Learning in Simulated Urban Environments. In: Bach, K., Ruocco, M. (eds) Nordic Artificial Intelligence Research and Development. NAIS 2019. Communications in Computer and Information Science, vol 1056. Springer, Cham. https://doi.org/10.1007/978-3-030-35664-4_4
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