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Robust Perception under Adverse Weather and Lighting Conditions

  • Existing visual perception methods scale badly to adverse weather and lighting conditions
  • Weather phenomenon simulation and image translation can generate effective training data for adverse conditions ?Other domain adaptation techniques such as domain flow and self-training can also increase the robustness of perception methods
  • New benchmarks with real-world data, such as our ACDC dataset, are strongly needed for method training and evaluation
  • Other robust sensors such as Radar and Microphones should be leveraged
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