How can machine vision algorithms be optimized for low-light and adverse weather conditions in autonomous driving?
Instruction: Describe techniques to improve the robustness of vision-based systems.
Updated
Example answer
Low light changes exposure, signal-to-noise ratio, contrast and motion blur. Start by identifying which task fails: lane markings, traffic signals, object detection or motion estimation. Inspect the actual sensor, optics, exposure settings and operating conditions before selecting a model change.
Longer exposure may collect more light but increase blur; gain may amplify noise; active illumination has range and environmental constraints. Thermal imaging can add evidence for some objects, but it does not necessarily show painted lane markings or visible signal colors. Complementary modalities need task-specific testing and correct calibration.
Training data should include relevant night conditions, glare, wet surfaces, unusual lighting and transitions. Synthetic darkening is not a complete model of sensor noise or optical effects. Apply augmentations that preserve labels and compare their effect on independent real sequences, including rare hazards.
Changing a confidence threshold trades missed detections against false alerts and may alter calibration. Measure both with task timing and downstream consequences; a lower threshold does not preserve accuracy automatically. Define the conditions in which the remaining capability is outside its validated limits and evaluate the specified response.