Vehicle Action Prediction Using Artificial Intelligence

Kevin Meng, Cheng Shi, Yu Meng

Abstract: Each year, car accidents on United States roadways claim tens of thousands of lives and injure millions of others, of which almost half involve a combination of two critical pre-crash events: turning and changing lanes. Advanced Driver Assistance Systems (ADAS) that are currently installed in vehicles provide reactive protections that warn drivers of dangers up to 0.5 seconds ahead of collisions. However, rule of thumb suggests 2 seconds for safety in emergency reactions; many lives could be saved even with a slight improvement to the warning time. This paper develops an innovative two-stage neural network model that predicts drivers' actions before fatal collisions can occur. In a novel procedural flow, data is collected from sensors and devices installed inside and outside the vehicle including two cameras, a Global Positioning System (GPS) module, an Onboard Diagnostics-II (OBD-II) interface, and a gyroscope, preprocessed with a Convolutional Neural Network-based (CNN) computer vision model to extract facial movements and rotation, filtered and selected with the Classification and Regression Tree (CART), and modeled with a Recurrent Neural Network w/ Long Short-Term Memory (RNN-LSTM). Results show that the methodology presented in this paper is superior compared to existing ones.