Safe Driving Behavior Research Articles

Factors Influencing Safe Driving Behaviors: Focusing on the Effects of Traffic Violations and Gender Differences

Factors Influencing Safe Driving Behaviors: Focusing on the Effects of Traffic Violations and Gender Differences

Vanets Technology for Improving Safe Driving Behavior

Vanets Technology for Improving Safe Driving Behavior

The Effects of Cognitive and Visual Functions of Korean Elderly Taxi Drivers on Safe Driving Behavior.

PurposeIn this study, we investigated the effect of visual and cognitive functions of elderly taxi drivers on safe driving behavior. We aimed to identify factors that interfere with safe driving in an aging Korean society in elderly taxi drivers.Participants and MethodsA total of 203 elderly taxi drivers, aged >65, working at 3 companies in a single city were assessed over 4 weeks from December 1 to December 30, 2017, using the Motor-Free Visual Perception Test, Korean Montreal Cognitive Assessment, and Korean Safe Driving Behavior Measure. To examine the effects of cognitive and visual functions on driving behavior, we performed a stepwise multiple linear regression analysis (p<0.05).ResultsAll 4 subdomains of safe driving behaviors were significantly correlated with the cognitive subdomains of attention and abstraction and the visual perception subdomains of visual closure 1 and figure-ground.ConclusionMore systematic assessments of the relationship between driving behavior and cognitive and visual function in elderly individuals are needed.

The Effect of Educational Intervention on Safe and High-Risk Driving Behaviors in Taxi Drivers

Background: High-risk driving behaviors is one of the leading causes of death and disability. Objectives: The aim of this study was to determine the effect of educational intervention on promoting safe-driving behaviors and reducing high risk-driving behaviors in taxi drivers based on the health belief model and planned behavior theory. Methods: A quasi-experimental study of interventional and control drivers (n = 40) selected by a cluster sampling method was conducted. The participants were selected from taxi stations. The intervention group was divided into 4 groups, including 10 people. The contents of the training program were based on driving laws, avoiding high-risk behaviors, and advising on safe driving behaviors. The driving behaviors were measured at baseline and 3-month post-intervention. Constructs of the health belief model and theory of planned behavior were used as an interventional program framework. Independent t-test and Paired t-test were used to compare the scores between intervention and control drivers and the intervention group before and after the intervention at each of the variables, respectively. Results: Three months post-intervention, the scores of safe driving behaviors in the intervention group were higher than the control group, and high-risk driving behaviors in the intervention group were less than the control group. After the intervention, a significant difference was observed in the mean scores of perceived barriers, self-efficacy, cues to action, attitude, subjective norms, and perceived behavioral control between two groups (P &lt; 0.05). Conclusions: Educational intervention within the framework of the combined constructs of the health belief model and theory of planned behavior can reduce high-risk driving behaviors and promote safe driving behaviors in taxi drivers.

Preliminary Study for Feasibility of Driver Agent in Actual Car Environment&amp;lt;br/&amp;gt;—Driver Agent for Encouraging Safe Driving Behavior (3)

Japan has become a more aged society and there are more drivers, 65 years of age and above. Cars represent an important mode of transportation for the elderly; however, in recent years, the number of traffic accidents caused by elderly drivers has been on the rise, and this has become a social issue. Thus, for the elderly drivers to encourage them to improve their driving, we study a driver agent system which consists of smartphone, communication robot and cloud service and provides the driving support by attention awakening and the feedback support based on driving behavior evaluation. In this paper, we presented a summary of the proposed agent and reported on a set of preliminary experiments using our agent in an actual car environment. From the analysis of subjective evaluations and fixation points during driving, the results revealed the possibility that the drivers accept the agent and supports from the agent during driving and that the agent in an actual car environment did not distract the driver.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

Study on Driver Agent Based on Analysis of Driving Instruction Data — Driver Agent for Encouraging Safe Driving Behavior (1) —

Study on Driver Agent Based on Analysis of Driving Instruction Data — Driver Agent for Encouraging Safe Driving Behavior (1) —

한국형 노인 안전운전행동 측정도구(K-SDBM)의 수정 및 보완의 필요성에 관한 연구

한국형 노인 안전운전행동 측정도구(K-SDBM)의 수정 및 보완의 필요성에 관한 연구

Prediction of taxi drivers' safe-driving behaviors based on the theory of planned behavior: The role of habit.

BACKGROUND:Safe-driving behaviors of taxi drivers are fundamental to health. The present research aimed to predict the taxi drivers' safe-driving behaviors based on the theory of planned behavior (TPB) and habit.MATERIALS AND METHODS:The present research is of a descriptive/analytical cross-sectional type conducted on 184 taxi drivers in Bandar Abbas who were selected through a multiple-stratified sampling method. Data collection instrument was a questionnaire comprised of two sections (demographic information and the constructs of TPB.STATISTICAL ANALYSIS USED:The data were later on analyzed via SPSS ver 19 and Pearson's correlation coefficient as well as multiple regressions.RESULTS:The mean age of the participants was 45.1 (standard deviation [SD = 11.1) years, and they had an average experience of taxi driving for 10.3 years (SD = 7.5). Subjective norms, perceived behavioral control, and habits were the predictors of one's intention of driving safely (r2= 0.30, F = 18.7, P < 0.001). Moreover, attitude, perceived behavioral control, and habits were found to be the predictors of safe-driving behaviors (r2= 0.19, F = 8.1, P < 0.001). Finally, habits showed to be a stronger predictor of safe-driving behaviors than attitude and perceived behavioral control.CONCLUSION:Consideration of individuals' behavioral habits and correction of unsafe habits, focus on the adverse effects of unsafe-driving behaviors, goal setting to change incorrect driving habits, attention to influential groups in altering unsafe-driving behaviors, and careful monitoring of abiding by the rules are suggested to promote safe-driving behaviors.

Effect of Difference in Form of Driving Support Agent to Driver’s Acceptability&amp;lt;br/&amp;gt;—Driver Agent for Encouraging Safe Driving Behavior (2)

In recent years, the number of traffic accidents caused by elderly drivers has increased in Japan. However, a car is an important mode of transportation for the elderly. Therefore, to ensure safe driving, a system that can assist elderly drivers is required. In this study, we propose a driver-agent system that provides support to elderly drivers during and after driving and encourages them to improve their driving. This paper describes the prototype system based on the analysis of the teaching records of a human instructor, and the subjective evaluation of driving support to elderly and non-elderly driver from three different agent forms, a voice, visual, and robot. The result revealed that the robot form is more noticeable, familiar, and acceptable to the elderly and non-elderly than other forms.

Effect of Psychological Factors on Safe Driving Behavior in Elderly

Effect of Psychological Factors on Safe Driving Behavior in Elderly

Validity and reliability of the safe driving behavior measure in community-dwelling self-drivers with stroke.

[Purpose] Driving is a vital component of recovery for stroke survivors facilitating restoration of their family roles and reintegration back into their communities and associations. The purpose of this study was to evaluate the validity and reliability of the Safe Driving Behavior Measure (SDBM) in community-dwelling self-drivers post-stroke. [Subjects and Methods] Participants were sixty-seven community-dwelling self-drivers who had received a diagnosis of first stroke in the past twelve months. To investigate the validity and reliability of the SDBM, this study evaluated two sessions, held three days apart in a quiet and well-organized assessment room. Cronbach’s alpha and the Intraclass Correlation Coefficient [ICC (2.1)] were used to evaluate statistically concurrent validity and reliability of the overall and three domain scores. Pearson’s correlations were used to quantify the bivariate associations among the three domains. [Results] The Cronbach’s alpha coefficients for the three domains of person-vehicle (0.989), person-environment (0.997), and person-vehicle-environment (0.968) of the SDBM indicated high internal consistency in community-dwelling self-drivers with stroke, in addition to excellent rest-retest reliability. [Conclusion] The results of this study suggest that the SDBM could be a reliable measure to evaluate automobile driving in community-dwelling self-drivers with stroke.

Sensitivity and specificity of the safe driving behavior measure and the driving habits questionnaire for older self-drivers.

[Purpose] To evaluate the sensitivity and specificity of the Safe Driving Behavior Measure and the Driving Habits Questionnaire in community-dwelling older self-drivers. [Subjects and Methods] Forty-five older participated in this study, to measure the Safe Driving Behavior Measure and the Driving Habits Questionnaire. Sensitivity and specificity were calculated along with cut-off values and overall accuracy of each measure as determined by the participants operating characteristic curve and the area under the curve. Multivariate logistic regression analysis was employed to identify predictors of driving abilities. [Results] The sensitivities were 0.538 for Safe Driving Behavior Measure, and 0.577, 0.423, and 0.615 for the difficulty, crash and citations, and driving space on domains of the Driving Habits Questionnaire, respectively. The specificities of the person-vehicle domain, person-environment domain, and person-vehicle-environment domain of the Safe Driving Behavior Measure were 0.474, 0.526, and 0.421, respectively, while the Driving Habits Questionnaire domains, the specificities of difficulty, crash and citations, and driving space were 0.526, 0.211, and 0.421, respectively. [Conclusion] The results of this study suggest that factors related to the accident history of older self-drivers were not well-explained, although the Safe Driving Behavior Measure and Driving Habits Questionnaire domains have the potential to determine driving-related accident history.

Test-retest reliability of the safe driving behavior measure for community-dwelling elderly drivers.

[Purpose] The Safe Driving Behavior Measure (SDBM) is a self-report measurement tools that assesses the safe-driving behaviors of the elderly. The purpose of this study was to evaluate the test-retest reliability of the SDBM among community-dwelling elderly drivers. [Subjects and Methods] A total of sixty-one community-dwelling elderly were enrolled to investigate the reliability of the SDBM. The SDBM was assessed in two sessions that were conducted three days apart in a quiet and well-organized assessment room. That test-retest reliability of overall scores and three domain scores of the SDBM were statistically evaluated using intraclass correlation coefficients [ICC (2.1)]. Pearson correlation coefficients were used to quantify bivariate associations among the three domains of the SDBM. [Results] The SDBM demonstrated excellent rest-retest reliability for community-dwelling elderly drivers. The Cronbach alpha coefficients of the three domains of person-vehicle (0.979), person-environment (0.944), and person-vehicle-environment (0.971) of the SDBM indicate high internal consistency. [Conclusion] The results of this study suggest that the SDBM is a reliable measure for evaluating the safe- driving of automobiles by community-dwelling elderly, and is adequate for detecting changes in scores in clinical settings.

한국형 자가보고식 노인 안전운전행동 측정도구(K-SDBM) 개발 및 신뢰도 연구

한국형 자가보고식 노인 안전운전행동 측정도구(K-SDBM) 개발 및 신뢰도 연구

Modeling for Vehicles' Safe Driving Behavior for Pavements Damage

Based on the domestic and foreign studying of vehicles lane-changing micro behavior theory of modeling and simulation, the model for vehicles safe driving behavior for pavements damage is established. A Logit-like discrete choice model for lane-changing is described under the constraint of pavements damage. The mechanism of vehicles lane-changing is analyzed. Calculation method of the minimum longitudinal spacing is established, which guarantees the no overlap between the vehicle track and pavements damage. The method can improve the safety and comfort during the lane-changing maneuvers. In future research, the model can provide warnings or take evasive actions to avoid risk for vehicles safe driving behavior.

Concurrent Criterion Validity of the Safe Driving Behavior Measure: A Predictor of On-Road Driving Outcomes

We determined the concurrent criterion validity of the Safe Driving Behavior Measure (SDBM) for on-road outcomes (passing or failing the on-road test as determined by a certified driving rehabilitation specialist) among older drivers and their family members-caregivers. On the basis of ratings from 168 older drivers and 168 family members-caregivers, we calculated receiver operating characteristic curves. The drivers' area under the curve (AUC) was .620 (95% confidence interval [CI] = .514-.725, p = .043). The family members-caregivers' AUC was .726 (95% CI = .622-.829, p ≤ .01). Older drivers' ratings showed statistically significant yet poor concurrent criterion validity, but family members-caregivers' ratings showed good concurrent criterion validity for the criterion on-road driving test. Continuing research with a more representative sample is being pursued to confirm the SDBM's concurrent criterion validity. This screening tool may be useful for generalist practitioners to use in making decisions regarding driving.

Self-Coaching System Based on Recorded Driving Data: Learning From One's Experiences

This paper describes the development of a self-coaching system to improve driving behavior by allowing drivers to review a record of their own driving activity. By employing stochastic driver-behavior modeling, the proposed system is able to detect a wide range of potentially hazardous situations, which conventional event data recorders are not able to capture, including those involving latent risks, of which drivers themselves are unaware. By utilizing these automatically detected hazardous situations, our web-based system offers a user-friendly interface for drivers to navigate and review each hazardous situation in detail (e.g., driving scenes are categorized into different types of hazardous situations and are displayed with corresponding multimodal driving signals). Furthermore, the system provides feedback on each risky driving behavior and suggests how users can safely respond to such situations. The proposed system establishes a cooperative relationship between the driver, the vehicle, and the driving environment, leading to the development of the next generation of safety systems and paving the way for an alternative form of driving education that could further reduce the number of fatal accidents. The system's potential benefits are demonstrated through preliminary extensive evaluation of an on-road experiment, showing that safe-driving behavior can be significantly improved when drivers use the proposed system.

Psychometrics of the Self-Report Safe Driving Behavior Measure for Older Adults

We investigated the psychometric properties of the 68-item Safe Driving Behavior Measure (SDBM) with 80 older drivers, 80 caregivers, and 2 evaluators from two sites. Using Rasch analysis, we examined unidimensionality and local dependence; rating scale; item- and person-level psychometrics; and item hierarchy of older drivers, caregivers, and driving evaluators who had completed the SDBM. The evidence suggested the SDBM is unidimensional, but pairs of items showed local dependency. Across the three rater groups, the data showed good person (≥3.4) and item (≥3.6) separation as well as good person (≥.93) and item reliability (≥.92). Cronbach's α was ≥.96, and few items were misfitting. Some of the items did not follow the hypothesized order of item difficulty. The SDBM classified the older drivers into six ability levels, but to fully calibrate the instrument it must be refined in terms of its items (e.g., item exclusion) and then tested among participants of lesser ability.

Rater Reliability and Rater Effects of the Safe Driving Behavior Measure

We used Safe Driving Behavior Measure (SDBM) to determine rater reliability and rater effects (erratic responses, severity, leniency) in three rater groups: 80 older drivers (mean age = 73.26, standard deviation = 5.30), 80 family members or caregivers (age range = 20-85 yr), and two driving evaluators. Rater agreement was significant only between the evaluators and the family members or caregivers. Participants rated driving ability without erratic effects. We observed an overall rater effect only between the evaluator and family members or caregivers, with the evaluators being the more severe rater group. Training family members or caregivers to rate driving behaviors more consistently with the evaluator's ratings may enhance the SDBM's usability and provide a role for occupational therapists to interpret proxy reports as an entry point for logical and efficient driving safety interventions.