Poster: Edge-Assisted Unsafe Driving Detection

Modern cars can detect unsafe driving by comparing the observed behavior of the subject vehicle (i.e., rear vehicles) with normal driving. However, normal driving does not have a standard definition. It changes depending on the situation. In this work, we address this problem and propose edge-assisted unsafe driving detection. In our proposal, instead of learning normal driving, the edge infers the most common unsafe driving patterns. It then shares this knowl-edge with cars. Cars look for such patterns to detect unsafe driving. Analysis of real-world traffic data shows that edge-assisted unsafe driving detection could detect unsafe behavior of subject vehicles with 90% accuracy.