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Learning to Drive Fast on a DuckieTown Highway

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Intelligent Autonomous Systems 16 (IAS 2021)


We train a small Nvidia AI JetRacer to follow the road on a small DuckieTown highway. In the real-world, roads do not always have the same appearance, so the system should not be trained on lane markings alone but on the complete view of the front camera. To make this possible, the system is trained in simulation using a recent reinforcement learning approach in an end-to-end fashion. This driving experience is then transferred to the circumstances encountered on a real track. Transfer learning is surprisingly successful, although this method is very sensitive to the details of the vehicle dynamics. We trained multiple models at different speeds and evaluated their performance both in simulation and in the real world. Increasing the velocity proves difficult, as the learned policy breaks down at higher speeds. The result is a small Nvidia AI JetRacer, which is able to drive around a DuckieTown highway, based on simulated experiences.

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    Supported by the Meaningful Control of Autonomous Systems initiative from TNO, CWI and UvA.

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    Driving on the Zigzag map used for training:

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    Driving on the unseen Udem1 map:

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    Driving in the real-world Duckietown Highway:


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Many thanks to Douwe van der Wal and Thomas van Orden for the interesting discussions and helpful suggestions.

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Correspondence to Thomas P. A. Wiggers .

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Wiggers, T.P.A., Visser, A. (2022). Learning to Drive Fast on a DuckieTown Highway. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham.

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