Abstract
Deep reinforcement learning has received wide attentions recently. It combines deep learning with reinforcement learning and shows to be able to solve unprecedented challenging tasks. This paper proposes an efficient approach based on deep reinforcement learning to tackle the road tracking problem arisen from self-driving car applications. We propose a new neural network which collects input states from forward car facing views and produces suitable road tracking actions. The actions are derived from encoding the tracking directions and movements. We perform extensive experiments and demonstrate the efficacy of our approach. In particular, our approach has achieved 93.94% driving accuracy, outperforming the state-of-the-art approaches in literature.
T. Chen is supported by EPSRC grant (EP/P00430X/1), ARC Discovery Project (DP160101652, DP180100691), and NSFC grant (No. 61662035). R. Al-Nima and T. Han are supported by EPSRC grant (EP/P015387/1).
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Al-Nima, R.R.O., Han, T., Chen, T. (2020). Road Tracking Using Deep Reinforcement Learning for Self-driving Car Applications. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_12
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