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Found 5 papers, 1 papers with code
Hierarchical Road Topology Learning for Urban Map-less Driving
The majority of current approaches in autonomous driving rely on High-Definition (HD) maps which detail the road geometry and surrounding area.
Holistic Grid Fusion Based Stop Line Estimation
Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems.
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications.
The Stixel world: A medium-level representation of traffic scenes
We believe this challenge should be faced by introducing a representation of the sensory data that provides compressed and structured access to all relevant visual content of the scene.
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications.
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