A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System

Abstract

In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.

Study on the Take‐over Performance of Level 3 Autonomous Vehicles Based on Subjective Driving Tendency Questionnaires and Machine Learning Methods

“…Using previously reported studies on the effect of providing driving situation information [5], providing a precue [6], using visual/auditory/tactile modality [7], and driving readiness [8,9], a method for the driver to quickly recognize TOR information and improve control transition performance was presented.…”

“…Thus, providing a precue before TOR may reduce response time. Kim and others [6] demonstrated that the three types of precues were provided 4 s before TOR. Visual channel precue was provided using repeated display while monophonic and repeated sounds represented auditory channel.…”

“…Various previous studies have aimed to improve the time performance. The provision of driving situation information was studied by Kim and others [5], and Kim and others [6] investigated a way of providing precue before initiating TOR. Cunninghama and Regana [7] examined the modality of an interactive device to provide a TOR, whereas Kim and others [8] and Endsley [9] studied the effects of driver readiness.…”

Autonomous Vehicles Technological Trends

“…It is not a coincidence since artificial intelligence has invaded practically all fields of engineering research in the last decade, particularly since the rise of deep learning. Some of the techniques presented in this issue include metaheuristics [4,5] for improving aspects of control, artificial neural networks for perception [6,7], applications in specific environments such as traffic circles [7] or intersections [8] and prediction [9][10][11], and driving behavior modelling [12][13][14].…”

Implementation of an Autonomous Overtaking System Based on Time to Lane Crossing Estimation and Model Predictive Control

“…To derive the lane information, the lane detection scheme is typically used to localize the lane boundaries in the given road images from a front view camera. Most of the existing lane detection methods are designed for advanced driver assistance systems (ADASs), such as lane departure warning systems (LDWs) or lane departure prevention systems (LDPs) [30]. Generally, the lateral distance to line crossing (DLC), i.e., lateral deviation from the lane border, to a lane boundary is used as a warning criterion.…”