Machine Learning Models for Road Surface and Friction Estimation using Front-Camera Images
Paper i proceeding, 2018

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, convolutional
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 snow/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

convolutional neural network

classification

Deep learning

features

drivable surface

Författare

Sohini Roy Chowdhury

Volvo Cars

Niklas Ohlsson

Volvo Cars

Minming Zhao

Volvo Cars

Andreas Wallin

Volvo Cars

Mats Jonasson

Chalmers, Mekanik och maritima vetenskaper, Fordonsteknik och autonoma system

Styrkeområden

Transport

Ämneskategorier

Fjärranalysteknik

Infrastrukturteknik

Datorseende och robotik (autonoma system)

DOI

10.1109/IJCNN.2018.8489188

Mer information

Senast uppdaterat

2019-05-21