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About this paper
Marcel Sheeny, Emanuele De Pellegrin, Saptarshi Mukherjee, Alireza Ahrabian, Sen Wang, Andrew Wallace
Heriot-Watt University researchers introduce RADIATE, a comprehensive multi-modal dataset collected in diverse, challenging weather conditions for autonomous driving perception. Baseline experiments demonstrate that radar-based object detection performance is marginally affected by adverse weather, particularly excelling in fog and night scenarios where optical sensors often fail.
Key insights
- Radar's inherent robustness to atmospheric obscurants makes it a critical sensor for enabling all-weather autonomous driving, complementing or replacing optical sensors when they fail.
- High-resolution scanning radar data, when appropriately annotated, can effectively support complex perception tasks such as object detection and tracking.
- The availability of a comprehensive and diverse dataset like RADIATE is essential to accelerate research and development of robust perception algorithms that can operate safely in real-world challenging conditions.
Summary by alphaXiv.
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