Autonomous braking system via deep reinforcement learning

Hyunmin Chae, Chang Mook Kang, Byeoung Do Kim, Jaekyum Kim, Chung Choo Chung, Jun Won Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors. The problem of designing brake control is formulated as searching for the optimal policy in Markov decision process (MDP) model where the state is given by the relative position of the obstacle and the vehicle's speed, and the action space is defined as the set of the brake actions including 1) no braking, 2) weak, 3) mid, 4) strong brakiong actions. The policy used for brake control is learned through computer simulations using the deep reinforcement learning method called deep Q-network (DQN). In order to derive desirable braking policy, we propose the reward function which balances the damage imposed to the obstacle in case of accident and the reward achieved when the vehicle runs out of risk as soon as possible. DQN is trained for the scenario where a vehicle is encountered with a pedestrian crossing the urban road. Experiments show that the control agent exhibits desirable control behavior and avoids collision without any mistake in various uncertain environments.

Original languageEnglish
Title of host publication2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538615256
DOIs
StatePublished - 2018 Mar 14
Event20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 - Yokohama, Kanagawa, Japan
Duration: 2017 Oct 162017 Oct 19

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2018-March

Other

Other20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
CountryJapan
CityYokohama, Kanagawa
Period17/10/1617/10/19

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  • Cite this

    Chae, H., Kang, C. M., Kim, B. D., Kim, J., Chung, C. C., & Choi, J. W. (2018). Autonomous braking system via deep reinforcement learning. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017 (pp. 1-6). (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2017.8317839