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Article

Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection

1
Department of Automotive Engineering, Hanyang University, Seoul 04763, Korea
2
Chassis System Control Development Team, Hyundai Motor Company, Gyeonggi-do 18280, Korea
3
Autonomous Vehicle Technology Laboratory, SW Part, CTO, LG Electronics, Seoul 07796, Korea
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4342; https://doi.org/10.3390/s18124342
Received: 29 November 2018 / Revised: 6 December 2018 / Accepted: 7 December 2018 / Published: 9 December 2018
Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memory (LSTM) unit and ensemble learning are utilized for network design and a feature selection technique is applied such that unnecessary sensor data could be excluded without a loss of performance. Real vehicle experiments were carried out for the learning and verification of the proposed deep learning structure. The classification performance was verified through four different test roads. The proposed network shows the classification accuracy of 94.6% in the test data. View Full-Text
Keywords: road classification; ensemble learning; recurrent neural network; feature selection road classification; ensemble learning; recurrent neural network; feature selection
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MDPI and ACS Style

Park, J.; Min, K.; Kim, H.; Lee, W.; Cho, G.; Huh, K. Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection. Sensors 2018, 18, 4342. https://doi.org/10.3390/s18124342

AMA Style

Park J, Min K, Kim H, Lee W, Cho G, Huh K. Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection. Sensors. 2018; 18(12):4342. https://doi.org/10.3390/s18124342

Chicago/Turabian Style

Park, Jongwon, Kyushik Min, Hayoung Kim, Woosung Lee, Gaehwan Cho, and Kunsoo Huh. 2018. "Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection" Sensors 18, no. 12: 4342. https://doi.org/10.3390/s18124342

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