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Found 7 papers, 2 papers with code
AutoGraph: Predicting Lane Graphs from Traffic Observations
To overcome this limitation, we propose to use the motion patterns of traffic participants as lane graph annotations.
Learning and Aggregating Lane Graphs for Urban Automated Driving
To overcome these challenges, we propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph.
TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories
Robustly classifying ground infrastructure such as roads and street crossings is an essential task for mobile robots operating alongside pedestrians.
Self-Supervised Moving Vehicle Detection from Audio-Visual Cues
In extensive experiments carried out with a real-world dataset, we demonstrate that our approach provides accurate detections of moving vehicles and does not require manual annotations.
Lane Graph Estimation for Scene Understanding in Urban Driving
Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities.
Ranked #2 on
Lane Detection
on nuScenes
HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images
We avoid the expensive annotation of nighttime images by leveraging an existing daytime RGB-dataset and propose a teacher-student training approach that transfers the dataset's knowledge to the nighttime domain.
Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning
In this work, we propose a novel terrain classification framework leveraging an unsupervised proprioceptive classifier that learns from vehicle-terrain interaction sounds to self-supervise an exteroceptive classifier for pixel-wise semantic segmentation of images.
