SafeCam: Analyzing intersection-related driver behaviors using multi-sensor smartphones

A large number of car accidents occur at intersections every year mainly due to drivers' "illegal maneuver" or "unsafe behavior". To promote traffic safety, we present SafeCam, a smartphone-based system that jointly leverages vehicle dynamics and the real-time traffic control information (e.g., traffic signals) to detect and study driver dangerous behaviors at intersections. In particular, SafeCam uses embedded sensors (i.e., inertial sensors) on the phone to generate soft hints tracking different driving conditions while at the same time adopts vision-based algorithms to recognize intersection-related critical driving events including unsafe turns, running stop signs and running red lights. In order to improve the system efficiency, we utilize adaptive color filtering under two lighting conditions (e.g., sunny and cloudy) and deploy the subsampling methods to make a trade off between the detection rate and the processing latency. In the evaluation, we conduct real-road driving experiments involving 15 drivers and 6 vehicles. The experiment results demonstrate that SafeCam is robust and effective in real-road driving environments, and has great potential to alert drivers for their dangerous behaviors at intersections and at the same time help them shape safe driving habits. Our experiments also reveal several interesting findings. 1) On average a driver failed to fully stop at stop signs 3 times in a trip of 3.5 km. 2) 11 out of 15 participants have lane drifting problems when they are making turns in the test. 3) Drivers took longer braking time when they approached a stop sign than a red light.