Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon, Chang-Su Kim
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ReproduceCode
- github.com/dongkwonjin/eigenlanesOfficialIn paperpytorch★ 135
- github.com/dnjs3594/Eigencontourspytorch★ 27
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| CULane | Eigenlanes (ResNet-50) | F1 score | 77.2 | — | Unverified |
| CULane | Eigenlanes (ResNet-18) | F1 score | 76.5 | — | Unverified |
| TuSimple | Eigenlanes (ResNet-18) | Accuracy | 95.62 | — | Unverified |