  We address the curve lane detection problem which poses more realistic
challenges than conventional lane detection for better facilitating modern
assisted/autonomous driving systems. Current hand-designed lane detection
methods are not robust enough to capture the curve lanes especially the remote
parts due to the lack of modeling both long-range contextual information and
detailed curve trajectory. In this paper, we propose a novel lane-sensitive
architecture search framework named CurveLane-NAS to automatically capture both
long-ranged coherent and accurate short-range curve information while unifying
both architecture search and post-processing on curve lane predictions via
point blending. It consists of three search modules: a) a feature fusion search
module to find a better fusion of the local and global context for multi-level
hierarchy features; b) an elastic backbone search module to explore an
efficient feature extractor with good semantics and latency; c) an adaptive
point blending module to search a multi-level post-processing refinement
strategy to combine multi-scale head prediction. The unified framework ensures
lane-sensitive predictions by the mutual guidance between NAS and adaptive
point blending. Furthermore, we also steer forward to release a more
challenging benchmark named CurveLanes for addressing the most difficult curve
lanes. It consists of 150K images with 680K labels.The new dataset can be
downloaded at github.com/xbjxh/CurveLanes (already anonymized for this
submission). Experiments on the new CurveLanes show that the SOTA lane
detection methods suffer substantial performance drop while our model can still
reach an 80+% F1-score. Extensive experiments on traditional lane benchmarks
such as CULane also demonstrate the superiority of our CurveLane-NAS, e.g.
achieving a new SOTA 74.8% F1-score on CULane.
