- 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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