MELD3: Integrating Multi-Task Ensemble Learning for Driver Distraction Detection

Abstract

Detecting and alerting distracted drivers is crucial to prevent traffic accidents. Although numerous studies have been proposed that use deep learning methods to detect driver distraction, most of these approaches rely on single-perspective images, which can lead to reduced accuracy due to occlusions and adverse environmental conditions. To address these limitations, we introduce MELD3, a novel approach that integrates Multi-Task Learning and Ensemble Learning to improve driver distraction detection performance. MELD3 utilizes images captured from multiple perspectives of the driver and employs multi-task learning to generate improved distraction detection results for each viewpoint. These results are then combined using the soft voting ensemble learning technique to produce a more accurate final detection outcome. The experimental results demonstrate that MELD3 achieves a high driver distraction detection accuracy of 96.22%. These findings indicate that MELD3 can significantly improve traffic safety by effectively identifying various distracting behaviors and reducing the risk of distraction-related accidents. Furthermore, its ability to generalize across different scenarios and driving conditions makes it an effective tool for real-world applications, particularly for integration into advanced driver assistance systems, thereby promoting safer driving environments.

Keywords

• Driver distraction detection
• multi-task learning
• ensemble learning
• multi-gate mixture of experts
• impartial multi-task learning
• intelligent transportation