To evaluate the performance of the advanced driver assistant systems, such as lane departure warning systems (LDWs) and lane keeping assist systems (LKAs), a deep learning model is proposed to estimate the lateral distance between the vehicle and lane boundaries. The training of a deep learning model requires a large number of label images, but the generation of label images is time consuming and boring. Therefore, an improved image quilting algorithm based on a convolutional neural network is proposed. A lot of lane and asphalt pavement images can be synthesised using fewer images of a real road scene. Moreover, an algorithm that aims to automatically generate label images using lane and asphalt pavement images to satisfy the distribution of real scenes is proposed. Experimental results showed that the generated label images can be used to train a deep learning model, and the lateral distance can be estimated with a sub-centimetre precision, which can provide an effective benchmark for the road test of LDWs, LKAs and other driving assistant systems.
Lateral distance detection model based on convolutional neural network
IET Intelligent Transport Systems ; 13 , 1 ; 31-39
2018-05-15
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
lateral distance recognition , lane keeping assist systems , label image generation , real road scene , deep learning model , learning (artificial intelligence) , lane boundaries , LKAs , lane departure warning systems , neural nets , advanced driver assistant systems , LDWs , convolutional neural network , asphalt pavement image synthesis , improved image quilting algorithm , image recognition , driver information systems , lateral distance estimation
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