End-to-End Autonomous Driving Through Dueling Double Deep Q-Network

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Recent years have seen the rapid development of autonomous driving systems, which are typically designed in a hierarchical architecture or an end-to-end architecture. The hierarchical architecture is always complicated and hard to design, while the end-to-end architecture is more promising due to its simple structure. This paper puts forward an end-to-end autonomous driving method through a deep reinforcement learning algorithm Dueling Double Deep Q-Network, making it possible for the vehicle to learn end-to-end driving by itself. This paper firstly proposes an architecture for the end-to-end lane-keeping task. Unlike the traditional image-only state space, the presented state space is composed of both camera images and vehicle motion information. Then corresponding dueling neural network structure is introduced, which reduces the variance and improves sampling efficiency. Thirdly, the proposed method is applied to The Open Racing Car Simulator (TORCS) to demonstrate its great performance, where it surpasses human drivers. Finally, the saliency map of the neural network is visualized, which indicates the trained network drives by observing the lane lines. A video for the presented work is available online, https:// youtu.be/76ciJmIHMD8 orhttps://v.youku.com/v_show/id_ XNDM4ODc0MTM4NA==.html. .
Publisher
SPRINGERNATURE
Issue Date
2021-08
Language
English
Article Type
Article
Citation

AUTOMOTIVE INNOVATION, v.4, no.3, pp.328 - 337

ISSN
2096-4250
DOI
10.1007/s42154-021-00151-3
URI
http://hdl.handle.net/10203/288184
Appears in Collection
GT-Journal Papers(저널논문)
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