Development and Comparative Analysis of Advanced Deep Learning Techniques for Crash Prediction in Advanced Driver Support Systems

Abstract

Motor vehicle crashes claimed 38,800 lives and caused 4.4 million injuries in 2019 alone. Studies have shown that 94% of these crashes are because of driver errors. Such a huge contribution of driver errors to crashes indicates that efforts at improving safety should be directed toward both vehicles and drivers through advanced driver assistance systems (ADAS) and vehicular technologies. This study investigates the potential that real-time driver behavior data collected through vehicular technologies offer to predict crashes, as the first line of defense to avoid them. Three deep learning models were developed including multilayer perceptron neural networks (MLP-NN), long-short-term memory networks (LSTMN), and convolutional neural networks (CNN) using vehicle kinematics time series data extracted from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) dataset. The study builds on the hypothesis that crashes are preceded by turbulences that take place over time (turbulence horizon). If these turbulences are detected promptly they can help predict and avoid crashes. Several values were tested for the turbulence horizon and the prediction horizon (how long before the crash impact it can be predicted) to identify the optimal values. The results showed that the CNN model can predict all crashes with a 100% accuracy and zero false alarms 3 s before the crash impact time when a 6-s turbulence horizon is used. This outstanding performance demonstrates the developed model is a promising tool for implementation in ADAS.