Explaining SENE: Manifold Learning for Distracted Driving Analysis

My first research paper, published in Engineering Applications of Artificial Intelligence (2023), proposed SENE — a novel manifold learning technique for analyzing distracted driving.

We developed a method that learns spatio-temporal embeddings from driver behavior and road data, enabling interpretable risk mapping across urban areas.

Key takeaways:

• Combined spatio-temporal and praxeological features for the first time.
• Reduced high-dimensional driving data into meaningful manifolds.
• Achieved 91% accuracy in predicting distraction-related risk.

Link to the paper: DOI Youtube video : Video (coming soon)

Why it matters

Distracted driving is one of the top causes of accidents. SENE helps policymakers and insurance companies identify high-risk regions and understand why certain driving behaviors lead to accidents — not just that they do.