Machine Learning Models for Road Surface and Friction Estimation using Front-Camera Images

• 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

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