Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

TL;DR

This paper introduces eigenlanes, a data-driven approach for detecting diverse road lanes by representing them in a learned eigenlane space, enabling accurate detection of both curved and straight lanes.

Contribution

The paper presents a novel eigenlane-based representation and an anchor-based detection network, SIIC-Net, for structurally diverse lane detection, which improves detection performance.

Findings

Excellent detection performance demonstrated on diverse lane structures

Eigenlane space effectively captures lane variability

Proposed method outperforms existing approaches

Abstract

A novel algorithm to detect road lanes in the eigenlane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane matrix containing all lanes in a training set. Second, we generate a set of lane candidates by clustering the training lanes in the eigenlane space. Third, using the lane candidates, we determine an optimal set of lanes by developing an anchor-based detection network, called SIIC-Net. Experimental results demonstrate that the proposed algorithm provides excellent detection performance for structurally diverse lanes. Our codes are available at https://github.com/dongkwonjin/Eigenlanes.