Papers › Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon, Chang-Su Kim
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.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Lane Detection | CULane | Eigenlanes (ResNet-50) | F1 score | 77.2 | #38 of 63 | Archive leaderboard | report |
| Lane Detection | CULane | Eigenlanes (ResNet-18) | F1 score | 76.5 | #41 of 63 | Archive leaderboard | report |
| Lane Detection | TuSimple | Eigenlanes (ResNet-18) | Accuracy | 95.62% | #31 of 43 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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