• Journal of the Korean Society of Industry Convergence
• /
• v.5 no.3
• /
• pp.239-246
• /
• 2002

The propose of this study is to analysis driver's behavior of traveler information. This research made an attempt to explore driver's route change behavior in the en-route stage. Model were developed for each analysis with LIMDEP software which was developed by Willams H Greene. Commuters' transportation change in before trip stage are affected by their income, travel time, and incident information and constant of this model showed their reluctance of change mode. This was resulted from the inappropriateness of traffic information to general commuters which is the main target of traffic information.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2007.06a
• /
• pp.339-340
• /
• 2007

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.11
• /
• pp.173-182
• /
• 2021

In this paper, we analyzed driver's and passenger's motions that cause driver's distraction, and recognized 10 driver's behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analyzed papers, 10 driver's behaviors related to cell phones, which are the main causes of distraction, were recognized. The experiment was conducted based on about 100,000 image data. Features were extracted through SURF and tested with three models (CNN, ResNet-101, and improved ResNet-101). The improved ResNet-101 model reduced training and validation errors by 8.2 times and 44.6 times compared to CNN, and the average precision and f1-score were maintained at a high level of 0.98. In addition, using CAM (class activation maps), it was reviewed whether the deep learning model used the cell phone object and location as the decisive cause when judging the driver's distraction behavior.

• Journal of the Ergonomics Society of Korea
• /
• v.29 no.1
• /
• pp.17-24
• /
• 2010

There has been recent interest in intelligent vehicle technologies, such as advanced driver assistance systems (ADASs) or in-vehicle information systems (IVISs) that offer a significant enhancement of safety and convenience to drivers and passengers. However, unsuitable design of HMI (Human Machine Interface) must increase driver distraction and workload, which in turn increase the chance of traffic accidents. Distraction in particular often occurs under a heavy driving workload due to multitasking with various electronic devices like a cell phone or a navigation system while driving. According to the 2005 road traffic accidents in Korea report published by the ROad Traffic Authority (ROTA), more than 60% of the traffic accidents are related to driver error caused by distraction. This paper suggests the structure of vehicle environment for real-time driving behavior monitoring system while driving which is can be used the driver workload management systems (DWMS). On-road experiment results showed the feasibility of the suggested vehicle environment for driving behavior monitoring system.

• Journal of Auto-vehicle Safety Association
• /
• v.10 no.3
• /
• pp.32-37
• /
• 2018

Recently, the driver can be assisted by the advanced active safety devices such as ADAS from road traffic risks. With this system, driver and passenger may freed from can driving tasks or kept eyes on forward direction while on the road. Help from adoptive cruise control, auto parking and newly develped automated driving vehicles technologies, the driver positions will vary significantly from the current standard driver position during the travel time. On this hypothesis, the objective of this study is analyze the behavior and injuries of drivers in the event of frontal impact under these abnormal driver position. Based on the KNCAP frontal impact testing method, this simulation matrix was set-up with dummies of 5 th tile female Hybrid III dummy and 50 th tile male Hybrid III dummy. The small sedan type passenger car was modeled in this simulation. The series of simulation was performed to compare the injuries and behaviour of each dummy, varying the seating status and seat position of each dummy.

• Journal of the Korean Society of Safety
• /
• v.22 no.1 s.79
• /
• pp.61-66
• /
• 2007

Car crashes are the leading cause of death for persons of every age. Specially, human-related factor has been known to be the primary causal factor of such crashes than vehicle-and environmental-related factors. There are various studies to analyze driver's behavior and characteristics in driving for reducing the car crashes in many areas of car engineering, psychology, human factor, etc. However, there are almost no studies which analyze mainly the human errors in driving and estimate their probabilities in terms of human reliability analysis. This study estimates the probability of human error in driving, i.e. driver error probability. First, fifty driver errors are investigated through DBQ (Driver Behavior Questionnaire) revision and the error likelihoods in driving are collected which are judged by skillful drivers using revised DBQ. Next, these likelihoods are converted into driver error probabilities using the results that verbal probabilistic expressions are changed into quantitative probabilities. Using these probabilities we can improve the warning effects on drivers by indicating their driving error likelihoods quantitatively. We can also expect the reduction effects of car accident through controlling especially dangerous error groups which have higher probabilities. Like these, the results of this study can be used as the primary materials of safety education on drivers.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.14 no.4
• /
• pp.86-94
• /
• 2015

In recent days, a Connected Car has caught an attention of the motor companies and various industrial institutes such as communication company. An automobile is regarded as a device and has been developed as an interactive system because the system is connected with various device. This drives a new business item, too. As a new automatic car technology is emerging, a new type of car accident is appeared, too. So, many researches for preventing car accident comes from the driver's are carried out in many car related institutes. In this paper, we study a driver's driving workload and develop an algorithm that measures the driver's driving behavior. We can see that the developed algorithm runs well by the experiment of road test. This results affects various road condition, driver's driving behavior and load that reflects the driver's status.

• Journal of the Ergonomics Society of Korea
• /
• v.31 no.2
• /
• pp.371-377
• /
• 2012

Objective: The aim of this study is to investigate effect of driver's cognitive distraction on driver's physiological state and driving performance, and then to determine parameters appropriate for detecting the cognitive distraction. Background: Driver distraction is a major cause of traffic accidents and poses a serious threat to traffic safety due to ever increasing use of in-vehicle information systems and mobile phones during driving. Cognitive distraction, among four different types of distractions, prevents a driver from processing traffic information correctly and adapting to change in surround vehicle behavior in time. However, the cognitive distraction is more difficult to detect because it normally does not involve significant change in driver behavior. Method: A full-scale driving simulator was used to create virtual driving environment and situations. Participants in the experiment drove the driving simulator in three different conditions: attentive driving with no secondary task, driving and conducting secondary task of adding numbers, and driving and conducting secondary task of conversing with an experimenter. Parameters related with driver's physiological state and driving performance were measured and analyzed for their change. Results: The experiment results show that driver's cognitive distraction, induced by secondary task of addition and conversation during driving, increased driver's cognitive workload, and indeed brought change in driver's physiological state and degraded driving performance. Conclusion: The galvanic skin response, pupil size, steering reversal rate, and driver reaction time are shown to be statistically significant for detecting cognitive distraction. The appropriate combination of these parameters will be used to detect the cognitive distraction and estimate risk of traffic accidents in real-time for a driver distraction warning system.

• Journal of Advanced Navigation Technology
• /
• v.17 no.2
• /
• pp.165-170
• /
• 2013

In this paper, we propose the information acquisition and analysis system for a vehicle driver in order to provide the safe driving environments. We first define the list of reckless driving behaviors and propose the recognition system, which recognizes the reckless behaviors, by using the acquired information. The collaboration among the information acquisition, the analysis, and the behavior comparison modules increases the accuracy of the recognition rate. Our system alarms to a vehicle driver in order to notify the potential to confront the dangerous situation due to the abnormal or reckless driving behaviors.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.421-431
• /
• 2022

The purpose of this research is to determine the point of fixation of the driver during the process of driving. Based on the results of this research, the driving instructor can make a judgement on what the trainee stare on the most. Traffic accidents have become a serious concern in modern society. Especially, the traffic accidents among unskilled and elderly drivers are at issue. A driver should put attention on the vehicles around, traffic signs, passersby, passengers, road situation and its dashboard. An eye-tracking-based application was developed to analyze the driver's gaze behavior. It is a prototype for real-time eye tracking for monitoring the point of interest of drivers in driving practice. In this study, the driver's attention was measured by capturing the movement of the eyes in real road driving conditions using these tools. As a result, dwelling duration time, entry time and the average of fixation of the eye gaze are leading parameters that could help us prove the idea of this study.