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Found 3 papers, 2 papers with code
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction
Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yield a minimum of 4. 8 higger accuracy and 1. 1* lower latency over competing methods on the NuScenes dataset.
Contrastive Learning for Lane Detection via Cross-Similarity
CLLD is a novel multitask contrastive learning that trains lane detection approaches to detect lane markings even in low visible situations by integrating local feature contrastive learning (CL) with our new proposed operation cross-similarity.
Ranked #4 on
Lane Detection
on TuSimple
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection
Lane detection is one of the most fundamental tasks for autonomous driving.
