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“Ease of Driving” Road Classification for Night-time Driving Conditions
Technical Paper
2016-01-0119
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper is an extension of our previous work on the CHASE (Classification by Holistic Analysis of Scene Environment) algorithm, that automatically classifies the driving complexity of a road scene image during day-time conditions and assigns it an ‘Ease of Driving’ (EoD) score. At night, apart from traffic variations and road type conditions, illumination changes are a major predominant factor that affect the road visibility and the driving easiness. In order to resolve the problem of analyzing the driving complexity of roads at night, a brightness detection module is incorporated in our end-to-end nighttime EoD system, which computes the ‘brightness factor’ (bright or dark) for that given night-time road scene. The brightness factor along with a multi-level machine learning classifier is then used to classify the EoD score for a night-time road scene. Our end-to-end ‘Night-time EoD system’ is a real-time onboard system implemented and tested on road scene data collected in Japan. We have improved the scope of the CHASE algorithm for computing EoD score for night-time driving conditions by including a brightness detection module.
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Citation
Pillai, P., Yalla, V., and Oguchi, K., "“Ease of Driving” Road Classification for Night-time Driving Conditions," SAE Technical Paper 2016-01-0119, 2016, https://doi.org/10.4271/2016-01-0119.Also In
References
- Yalla Veeraganesh , Pillai Preeti J. and Oguchi Kentaro CHASE Algorithm: "Ease of Driving" Classification IEEE 18th International Conference on Intelligent Transportation Systems 2015
- http://www.mlit.go.jp/road/road_e/pdf/ROAD2014web.pdf
- https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/315783/road-classification-guidance.pdf
- Bosch , A. , Zisserman , A. , & Muñoz , X. 2006 Scene classification via pLSA Computer Vision-ECCV 2006 517 530 Springer Berlin Heidelberg
- Borji , A. , & Itti , L. 2011 May Scene classification with a sparse set of salient regions Robotics and Automation (ICRA), 2011 IEEE International Conference on 1902 1908 IEEE
- Oliva , Aude , and Torralba Antonio Building the gist of a scene: The role of global image features in recognition Progress in brain research 155 2006 23 36
- Cortes , C. ; Vapnik , V. 1995 Support-vector networks Machine Learning 20 3 273 10.1007/BF00994018
- Bradski , G. 2000 The OpenCV Library (2000) Dr. Dobb's Journal of Software Tools
- Chang , C. C. , & Lin , C. J. 2011 LIBSVM: a library for support vector machines ACM Transactions on Intelligent Systems and Technology (TIST) 2 3 27