The main cause of traffic accidents is drivers' human errors such as cognitive, judgment, and execution errors. To mitigate drivers' human errors, research on the measurement and quantification of driver workload as well as the development of smart vehicles is needed. Drivers' behavior while driving includes driving straight, turning left or right, U-turns, rapid acceleration, rapid deceleration, and changing lanes. To measure and quantify a driving workload, both the subjective workload and the behavior workload caused by varied driving behaviors should be taken into account on the basis of understanding the visual, auditory, cognitive, and psychomotor characteristics of the driving workload. In this paper, we analyze electroencephalogram (EEG) data collected through an urban road driving test. To overcome large deviations of EEG values among drivers, we used EEG variation rates instead of raw EEG values. We extracted five kinds of behavior sections from the data: left-turn section, right-turn section, rapid-acceleration section, rapid-deceleration section, and lane-change section. We then selected a reference section for each of these behavior sections and compared EEG values from the behavior sections with those from the reference sections to calculate the EEG variation rates, after which we made the statistical analysis. The analysis results of this study are being used to explain the cognitive characteristics of a driving workload caused by drivers' behavior in the vehicle information system, which will provide information for safe driving by taking into account the driving workload.

Driving is an essential activity in today's busy and complex society, and it demands physical and mental abilities, collectively known as a driving workload. For safe and comfortable driving, it would be useful to detect when drivers are being overloaded. Analyzing driver's workload using an electroencephalograph (EEG) is useful for this purpose. However, it is very inconvenient to obtain an EEG during actual driving, since the measuring device needs to be attached to the driver. In this paper, we develop a model to predict the driver's EEG level utilizing basic information obtained while the vehicle is being driven. We divided the EEG values into two classes, ``normal'' and ``overload'', and extracted useful features from the vehicle driving information, such as engine RPM, vehicle speed, lane changes, and turns. A classification model using a support vector machine was built to predict normal and overload states during actual driving. We evaluated the performance of the proposed method using field-of-test data collected when driving on actual roads, and suggest directions for future research based on an analysis of the experimental results.

The use of newly emerging sensor technologies in traditional roadway systems can provide real-time traffic services to drivers through Telematics and Intelligent Transport Systems (ITSs). This paper introduces a smart roadside system that utilizes various sensors for driver assistance and traffic safety warnings. This paper shows two road application models for a smart roadside system and sensors: a red-light violation warning system for signalized intersections, and a speed advisory system for highways. Evaluation results for the two services are then shown using a micro-simulation method. In the given real-time applications for drivers, the framework and certain algorithms produce a very efficient solution with respect to the roadway type features and sensor type use.

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.

The aim of this study is to find out whether the provision of situation awareness (SA) information is effective in reducing the transition time that the control authority is transited from automated driving to manual driving. In the level 3 automobile, the driver must remain in a fallback ready state in which he/she can drive at any time, so a human factor study of the driver’s SA is required. Thirty-six drivers licensed performed three experiment scenarios with an automated driving feature activated. Participants were asked to enter some sentence with his/her smartphone in the automated driving section. Situation information was provided differently for each stage of the experiment using the iPad installed in the vehicle. We measured the driver’s response time to the takeover request (TOR), the time to start the manual driving, and the stabilization time. We found that the time required to recognize and respond to TOR was reduced in the case of providing SA information. We recommend that level 3 automobile manufacturers design their vehicles to provide SA information to ensure the driver’s safety.