Polynomial Regression Network for Variable-Number Lane Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12363)


Lane detection is a fundamental yet challenging task in autonomous driving and intelligent traffic systems due to perspective projection and occlusion. Most of previous methods utilize semantic segmentation to identify the regions of traffic lanes in an image, and then adopt some curve-fitting method to reconstruct the lanes. In this work, we propose to use polynomial curves to represent traffic lanes and then propose a novel polynomial regression network (PRNet) to directly predict them, where semantic segmentation is not involved. Specifically, PRNet consists of one major branch and two auxiliary branches: (1) polynomial regression to estimate the polynomial coefficients of lanes, (2) initialization classification to detect the initial retrieval point of each lane, and (3) height regression to determine the ending point of each lane. Through the cooperation of three branches, PRNet can detect variable-number of lanes and is highly effective and efficient. We experimentally evaluate the proposed PRNet on two popular benchmark datasets: TuSimple and CULane. The results show that our method significantly outperforms the previous state-of-the-art methods in terms of both accuracy and speed.


Lane detection Polynomial curve Deep neural network Polynomial regression 



This work is supported by the National Natural Science Foundation of China under Grant 61673362 and 61836008, Youth Innovation Promotion Association CAS (2017496), and the Fundamental Research Funds for the Central Universities.

Supplementary material

504473_1_En_42_MOESM1_ESM.pdf (1.2 mb)
Supplementary material 1 (pdf 1214 KB)


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Science and Technology of ChinaHefeiChina

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