| Title | : | Towards Safer Driving: Anomaly Detection for Driver Distraction Using Unsupervised Learning |
| Speaker | : | Dr. Kankana Roy (Karolinska Institute, Sweden) |
| Details | : | Mon, 27 Oct, 2025 3:15 PM @ SSB233 |
| Abstract: | : | In this presentation, we will explore the problem of driver’s cellphone usage detection in vehicles for driver inattentiveness assessment. Current datasets in the literature, which are widely used in this domain to build and test models to be deployed in the real world, often suffer from class distribution bias, frequently overrepresenting inattentive behavior in the dataset to aid the supervised models. This imbalance may limit the generalizability of such models in practical, real-world driving conditions, where mobile usage is a relatively rare event. To address this challenge, we propose a novel approach using unsupervised dictionary learning by treating the problem as anomaly detection rather than supervised classification. This work illustrates one approach, combining hand detection, feature engineering, dictionary learning, and real-world applicability through a novel idea that reduces reliance on biased labels. Our results demonstrate comparable detection accuracy to fully supervised methods reported in the literature while offering better robustness to dataset imbalance. Overall, our study offers a practical and computationally efficient alternative to conventional supervised pipelines for driver inattentiveness detection, emphasizing generalizability and real-world feasibility. |
