Time-Dependency-Aware Driver Distraction Detection Using Linear-Chain Conditional Random Fields

Driver distraction is one of the main causes of traffic accidents, of which two critical types are cognitive distraction and visual distraction. To improve traffic safety, the functionality of detecting driver distraction is necessary for intelligent vehicles. However, while existing studies mainly applied classification-based methods, few efforts have been devoted on modelling the relationship between input features and time dependency of driver state, which is shown to be an effective way to improve accuracy. This study proposed a linear-chain conditional random fields (CRF) based approach to detect cognitive distraction and visual distraction. Experiment was carried out on a driving simulator to collect data, where n-back task and arrow task were used to induce cognitive and visual distraction, respectively. 4 types of interpretable features were applied, including mean of skin conductance level, standard deviation of horizontal gaze position, steering reversal rates and standard deviation of lateral position. The dynamic bayesian network (DBN) used in previous studies was introduced to be the baseline. Results showed that, the proposed CRF has a superior performance than DBN, with a holistic accuracy of 93.7% and average true positive rates of 91.2% and 89.2% for cognitive distraction and visual distraction, respectively. This performance gap is due to the incorporation of input features into the transition feature functions of the designed CRF, thus making it more suitable for modelling driver state transition pattern in real application.

I. Introduction

With the rapid development of intelligent vehicle technology, expectation soars that vehicles can improve traffic safety through various advanced functionalities. Driver distraction detection is the one among them due to the increased risk of causing accidents posed by driver distraction. According to latest statistics by National Highway Traffic Safety Administration, nine percent of fatal crashes were reported as distraction-affected crashes and 3,166 people killed in motor vehicle crashes involving distracted drivers in 2017 in America [1]. Even for the most advanced autonomous vehicles where most of motion control tasks are executed by driving automation system, driver distraction detection function is still essential for improving safety by making sure the backup driver is attentive. Example for evidence can be seen in [2]. In March 2018, Uber test self-driving vehicle collided with a pedestrian, causing the latter’s death. One of the causes of the accident is thought to be the distraction of the backup driver before collision. Therefore, driver distraction detection is an indispensable function for intelligent vehicle for a long time.