Understanding Adverse Weather Impacts on Perception
Self-driving cars rely on sensors like cameras, LiDAR, and radar for environmental perception. However, adverse weather conditions such as rain, fog, and snow can degrade sensor performance, leading to incomplete or erroneous data.
Key Challenges:
- Rain: Water droplets scatter light and obscure camera views, reducing object detection accuracy. For LiDAR, rain causes false positives by reflecting laser beams.
- Fog: Low visibility attenuates signals; for example, LiDAR range can drop from 200m in clear conditions to under 50m in dense fog.
- Snow: Accumulates on sensors, blocking fields of view and introducing noise in radar returns.
Consider a simple model for visibility degradation in fog: the contrast $C$ at distance $d$ follows Beer-Lambert law, $$C(d) = C_0 e^{-\beta d},$$ where $\beta$ is the extinction coefficient (higher in fog), and $C_0$ is initial contrast. This illustrates why perception algorithms must adapt.
Example Application: In rainy urban driving, a camera-based lane detection system might misinterpret wet road reflections as lane markings, risking deviation.