Sökning: WFRF:(Jonasson Mats 1969) >
Machine Learning Mo...
Machine Learning Models for Road Surface and Friction Estimation using Front-Camera Images
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- Chowdhury, Sohini Roy (författare)
- Volvo Cars
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- Ohlsson, Niklas (författare)
- Volvo Cars
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- Zhao, Minming (författare)
- Volvo Cars
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- Wallin, Andreas (författare)
- Volvo Cars
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- Jonasson, Mats, 1969 (författare)
- Volvo Cars
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(creator_code:org_t)
- 2018
- 2018
- Engelska.
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Ingår i: 2018 International Joint Conference on Neural Networks (IJCNN). - 2161-4407. - 9781509060146
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Automotive active safety systems can significantly benefit from real-time road friction estimates (RFE) by adapting driving styles, specific to the road conditions. This work presents a 2-stage approach for indirect RFE estimation using front-view camera images captured from vehicles. In stage-1, onvolutional neural network model architectures are implemented to learn region-specific features for road surface condition (RSC) classification. Texture-based features from the drivable surface, sky and surroundings are found to be separate regions of interest for dry, wet/water, slush and now/ice RSC classification. In stage-2, a rule-based model that relies on domain-specific guidelines is implemented to segment the ego-lane drivable surface into [5x3] patches, followed by patch classification and quantization to separate images with high, medium and low RFE. The proposed method achieves average accuracy of 97% for RSC classification in stage-1 and 89% for RFE classification in stage-2, respectively. The 2-stage models are trained using publicly available data sets to enable benchmarking
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Remote Sensing (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Infrastrukturteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Infrastructure Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Deep learning
- classification
- convolutional neural network
- features
- drivable surface
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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