•European naturalistic driving data reveal behavioral differences across countries.•European drivers do not necessarily behave like U.S. drivers.•European drivers use their mobile phones less while ...driving than U.S. drivers.•The UDRIVE dataset provides more in depth understanding of driver behavior.•Introduction of the conceptual Framework for Naturalistic Driving Studies (FNDS).•FNDS gives insight in scope of study in the relation to sample and data collected.
Within the UDRIVE project, a rich cross-European naturalistic driving database was created which includes everyday driving data on car and truck drivers and powered two-wheeler riders. The database provides extensive, reliable insights into driving behavior in real traffic as a foundation for improving the safety and sustainability of European road traffic. This paper discusses the characteristics of the data in the UDRIVE database—elucidating key methodological choices and presenting a selection of results to date.
A priority of the study design was obtaining in-depth information on driving behavior, permitting the exploration of diverse research questions. A tailor-made data acquisition system collected very comprehensive data. A total of 287 drivers/riders participated. The sample size restricts the addressable research topics to common behaviors in everyday driving and limits the generalizability of results. However, the data are extensive and promising analyses have already been performed. The results show differences between European countries for distracting activities, seatbelt use, and looking behavior towards cyclists at urban intersections. Moreover, it shows that European drivers engage less in mobile phone use than U.S. drivers. It is likely that European drivers differ in other ways, also—highlighting the dataset’s value for developing and implementing targeted safety measures, for the E.U. and its individual countries.
Based on the comparison of the different studies, the paper introduces the general conceptual framework for naturalistic driving studies, providing insight in the relation between the scope of a naturalistic driving study and the key methodological choices on sample selection and data acquisition system.
Human behavior is implicated in most road accidents. The current study examined drivers' behavior that interferes with decision making and reaction time to an incidence. Adults (≥17 years-old) ...participated in a questionnaire-based survey for driver's behavior. Dataset was weighed according to sex, age and education based on the 2011 census. Differences between groups were assessed with Chi-squared tests while logistic regression models were used to identify drivers' characteristics for specific behaviors. A total 1601 adults participated in the survey-48% males and 52% females. Texting, Global Positioning System (GPS) setting and smoking were observed more by professional drivers and drivers of an urban area, while smoking was also dependent on social class. Drink driving was observed more by males (20% vs. 5% females), while after adjusting for age, the odds of drink driving in males were 5 times higher than females (
< 0.001). A different effect of age depending on the driver's sex and vice versa was observed regarding phone calls. Drivers' behavior with distractive potential differed by age, sex, social class and area of residence. Male drivers were more likely to perform drink driving, while professional drivers were more likely to use cell phone for calls and texting, set the GPS and smoke while driving.
•The relationships between driving anger and driving outcomes were reviewed and meta-analysed.•Driving anger was a positive predictor of all three types of aberrant driving.•Driving anger was ...associated with increased risk of accident involvement.•Driving anger was a stronger predictor of risky driving among young drivers than among old drivers.
Through the use of meta-analysis, this study investigated the relationships between driving anger and five types of driving outcomes (aggressive driving, risky driving, driving errors, near misses and accidents). The moderating effects of three variables (age, study publication year, and participants’ country of origin) on these relationships were also examined. A total of 51 studies published over the past two decades met the inclusion criteria for the meta-analysis. The results showed that driving anger significantly predicted all three types of aberrant driving, with zero-order correlations of 0.312, 0.243, and 0.179 with aggressive driving, risky driving and driving errors, respectively. The correlations between driving anger and accident-related conditions, though at relatively weaker levels, were still statistically significant. Tests for effects of the moderating variables suggested that driving anger was a stronger predictor of risky driving among young drivers than among old drivers. Also, the anger–aggression association was found to decrease over time and vary across countries. The implications of the results and the directions for future research are discussed.
Objective:
The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road ...safety.
Background:
Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking.
Method:
A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety.
Results:
A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and sociocultural factors influencing driving style were reviewed.
Conclusion:
Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesize these results and make useful generalizations. There is a considerable potential for increasing road safety by means of behavior modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles.
Application:
Knowledge about driving styles can be applied in programs for modifying driver behavior and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems.
Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time ...when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.
Rationale
Although driving simulators (DS) are receiving increasing attention due to concern over traffic accidents under the influences of drugs, few DS are recognized for their reliability and ...validity. Therefore, the development of an evaluation system using DS for driving performance is urgently needed.
Objectives
To investigate whether the standard deviation of lateral position (SDLP) increases with blood alcohol concentration (BAC) using a DS with reliability and calculate the SDLP threshold from the difference between BAC levels of 0 and 0.05%.
Methods
Twenty healthy Japanese men performed the DS tasks up to 60 min in Study 1 and DS tasks twice at 1-week intervals in Study 2. Twenty-six healthy men conducted the same DS tasks under BAC level (0, 0.025, 0.05, and 0.09%) in double-blind, randomized, crossover trial in Study 3. The primary outcome was SDLP in a road-tracking test. The test–retest reliability of DS data was assessed, and the estimated difference in SDLP between BAC levels of 0 and 0.05% was calculated using a linear regression model.
Results
The cumulative SDLP values at 5-min intervals were stable, and the intraclass correlation coefficient for its values was 0.93. SDLP increased with BAC in a concentration-dependent manner. The predicted ΔSDLP value for the difference between BAC levels of 0 and 0.05% was 9.23 cm. No participants dropped out because of simulator sickness.
Conclusions
The new DS used in these studies has reliability, validity, and tolerability and is considered suitable for evaluating the influence of drugs on driving performance.
Driving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency ...models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction.
Crash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models.
Model estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood.
The study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated.
•Real-time refined-scale data are incorporated to characterize the time-varying nature of these contributing factors•Unbalanced panel data Mixed logit models are developed to predict hourly crash likelihood•The estimation results show that a number of factors were related to crash likelihood on I-25•This study underscores the unique and significant impacts of the real-time weather, road surface and traffic conditions
Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data ...in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.
Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.
Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.
Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.
Investigate how novice drivers with autism spectrum disorder (ASD) differ from experienced drivers and whether virtual reality driving simulation training (VRDST) improves ASD driving performance. 51 ...novice ASD drivers (mean age 17.96 years, 78% male) were randomized to routine training (RT) or one of three types of VRDST (8–12 sessions). All participants followed DMV behind-the-wheel training guidelines for earning a driver’s license. Participants were assessed pre- and post-training for driving-specific executive function (EF) abilities and tactical driving skills. ASD drivers showed worse baseline EF and driving skills than experienced drivers. At post-assessment, VRDST significantly improved driving and EF performance over RT. This study demonstrated feasibility and potential efficacy of VRDST for novice ASD drivers.
PDF
