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Found 6 papers, 2 papers with code
Raw High-Definition Radar for Multi-Task Learning
With their robustness to adverse weather conditions and ability to measure speeds, radar sensors have been part of the automotive landscape for more than two decades.
Multi-View Radar Semantic Segmentation
Understanding the scene around the ego-vehicle is key to assisted and autonomous driving.
StyleLess layer: Improving robustness for real-world driving
Robustness to various image corruptions, caused by changing weather conditions or sensor degradation and aging, is crucial for safety when such vehicles are deployed in the real world.
PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain
In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation.
Deep Open Space Segmentation using Automotive Radar
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data
In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data.
