Estimation of Lead Vehicle Kinematics Using Camera-Based Data for Driver Distraction Detection

Abstract

Distracted driving has become an emerging concern for road safety in the past decade. Efforts have been made to develop in-vehicle active safety systems that could detect driver distraction. However, most methods focused on detecting a distracted driver of the host vehicle (ego-vehicle). Given that a distracted driver poses increased crash risk not only to him/herself but also to other road users, it may be beneficial to investigate ways to detect a distracted driver from a surrounding vehicle. This paper proposes a method to estimate the kinematics of a lead vehicle solely based on the sensory data from a host vehicle. The estimated kinematics of the lead vehicle include its lane position, lateral speed, longitudinal speed, and longitudinal acceleration, all of which may be potentially useful to detect distracted driving. The method was developed and validated using an existing naturalistic driving study, Safety Pilot Model Deployment, which collected a large scale of driving data in real-world roadways. The method utilizes signals from a camera-based Mobileye® system and other host vehicle sensory channels such as speed and yaw rate. Sensor fusion techniques were used to improve the accuracy of the estimation. The validation results show that the method was able to capture the lead vehicle’s kinematics within a fairly small error range. The method could be potentially used to develop in-vehicle systems that are able to monitor the behaviors of its surrounding vehicles and detect distracted or impaired driving.