MOUNTAIN VIEW, CA
ITL Researcher – Seyhan Ucar received the Best Paper Award at the ITS World Congress.
Rear-end collisions are the most frequent type of collision in the USA, and most of them are due to the distracted driving behavior of follower vehicles. This paper analyzes driving data and infers movement patterns unique to distracted driving. It uses such movement patterns and detects unsafe follower vehicles in real-time. The ego vehicle notifies its driver (e.g., suggests a lane change) and avoids rear-end collision when an unsafe follower vehicle is behind.
Title: Mining Movement Patterns for Distracted Driving Detection
