Table 9.
DDD systems challenges.
| System Type | Imaged-Based | Biological-Based | Vehicle-Based | |
|---|---|---|---|---|
| Challenges | ||||
| Difficulty in extracting drowsiness signs, due to facial characteristics/skin color | High | N/A | N/A | |
| Difficulty in extracting drowsiness signs, due to objects that cover the face | High | N/A | N/A | |
| Driver’s posture and distance from the dashboard | High | Low | N/A | |
| Real-time video analysis | Medium | N/A | N/A | |
| Driver movement | High | High | N/A | |
| Noisy sensor measurements | Low | High | Low | |
| Monitoring equipment and sensors inconvenience | Low | Medium | Low | |
| Influence of environmental conditions (weather/illumination) | High | Low | Medium | |
| Influence of the road conditions and geometry | Low | Low | High | |
| Hardware complexity and limitations | Low | High | Low | |
| Drowsiness signs extraction precision | Low | Low | High | |
| Testing under real (not simulated) driving conditions | Medium | Medium | Medium | |