Publications (5)
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Florian Piewak, Timo Rehfeld, Michael Weber +1
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 appli…
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos +6
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, esp…
The Stixel world: A medium-level representation of traffic scenes
Marius Cordts, Timo Rehfeld, Lukas Schneider +5
Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that inter…
Hierarchical Road Topology Learning for Urban Map-less Driving
Li Zhang, Faezeh Tafazzoli, Gunther Krehl +4
The majority of current approaches in autonomous driving rely on High-Definition (HD) maps which detail the road geometry and surrounding area. Yet, this reliance is one of the obs…
Holistic Grid Fusion Based Stop Line Estimation
Runsheng Xu, Faezeh Tafazzoli, Li Zhang +3
Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an e…