scholarly journals Use of multilevel modeling to examine variability of distracted driving behavior in naturalistic driving studies

2021 ◽  
Vol 152 ◽  
pp. 105986
Author(s):  
Sara A. Freed ◽  
Lesley A. Ross ◽  
Alyssa A. Gamaldo ◽  
Despina Stavrinos
Keyword(s):  
Multilevel Modeling ◽  
Driving Behavior ◽  
Distracted Driving ◽  
Naturalistic Driving

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals INDIVIDUAL VALUE SYSTEM CHARACTERISTICS AND DRIVING BEHAVIOR RELATIONSHIPS

2017 ◽  
Vol 1 ◽  
pp. 570
Author(s):  
Krists Jānis Lazdiņš ◽  
Kristīne Mārtinsone
Keyword(s):  
Driving Behavior ◽  
Distracted Driving ◽  
Value System ◽  
System Integrity ◽  
Dangerous Driving ◽  
The Future ◽  
And Gender ◽  
The Individual ◽  
Individual Value ◽  
System Characteristics

The aim of research was to examine characteristics of individual value system prediction for driving behavior. It raised fundamental question for the research: 1. which of the individual value system characteristics predict driving behavior controlling gender and age. In the study participated 108 respondents, 40 (37.0%) men and 68 (63.0%) women who filled the questionnaire on the internet. There was used two questionnaires – „Latvian driving behavior survey”, The value and levels of availability relations in different spheres of life” The results showed that the value system integrity / disintegrity indicator predicts distracted driving, explains 18% of variation and is statistically significantly. Internal vacuum and age statistically significantly negatively predicts risky driving explaining 17% of variation. Age statistically significantly predicts safe and courteous driving, explains 12% of variation. Value system integrity / disintegrity indicator and gender, statistically significantly negatively predicts summary indicator of dangerous driving, explains 22% of variation. Age statistically significantly negatively predicts distracted driving, explains 30% of variation. Limitations of the research are related to the size of the sample, alignment of participants and use of new instruments, as well as data collection method. If the study would be repeated in the future, it would be desirable to increase the sample size and use approbated instrument. It would be interesting to find out how the value of individual factors predicts objective size of accidents and violations caused by driving. The results can serve as the basis to create new driving behavior interventions and also applicable to psychologist's professional work, when counseling individuals of this group, as well as can be used in the future development of the field, science and research.

Studying Driving Behavior on Horizontal Curves using Naturalistic Driving Study Data

2018 ◽  
Vol 2672 (17) ◽  
pp. 83-95 ◽  
Author(s):  
Bashar Dhahir ◽  
Yasser Hassan
Keyword(s):  
Data Collection ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Study Data ◽  
Output Analysis ◽  
Horizontal Curves ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Design Consistency ◽  
Favorable Weather

Many studies have been conducted to develop models to predict speed and driver comfort thresholds on horizontal curves, and to evaluate design consistency. The approaches used to develop these models differ from one another in data collection, data processing, assumptions, and analysis. However, some issues might be associated with the data collection that can affect the reliability of collected data and developed models. In addition, analysis of speed behavior on the assumption that vehicles traverse horizontal curves at a constant speed is far from actual driving behavior. Using the Naturalistic Driving Study (NDS) database can help overcome problems associated with data collection. This paper aimed at using NDS data to investigate driving behavior on horizontal curves in terms of speed, longitudinal acceleration, and comfort threshold. The NDS data were valuable in providing clear insight on drivers’ behavior during daytime and favorable weather conditions. A methodology was developed to evaluate driver behavior and was coded in Matlab. Sensitivity analysis was performed to recommend values for the parameters that can affect the output. Analysis of the drivers’ speed behavior and comfort threshold highlighted several issues that describe how drivers traverse horizontal curves that need to be considered in horizontal curve design and consistency evaluation.

scholarly journals NATURALISTIC DRIVING BEHAVIOR AS A NEUROBEHAVIORAL MARKER OF PRECLINICAL ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S886-S886
Author(s):  
Ganesh Babulal
Keyword(s):  
Driving Behavior ◽  
Receiver Operating Curve ◽  
Stepwise Logistic Regression ◽  
Prospective Longitudinal Study ◽  
Cognitively Normal ◽  
Preclinical Ad ◽  
Ε4 Allele ◽  
Naturalistic Driving ◽  
Prospective Longitudinal ◽  
Apolipoprotein Ε4

Abstract Decline in driving skills begins in preclinical AD, when an older adult remains cognitively normal, but the underlying disease process has begun. Preclinical AD is detectable among cognitively normal individuals using molecular biomarkers: positron emission tomography (PET) imaging and cerebrospinal fluid (CSF). The aim of this prospective, longitudinal study is to determine whether naturalistic driving behavior using in-vehicle dataloggers can distinguish older adults with (n=36) and without preclinical AD (n=134). Driving data was calculated as mean/month for several variables (number of trips/day, trip length, trip time, speeding, and hard-braking) for participants followed between one to 46 months. Using stepwise logistic regression, the area under the receiver operating curve (AUC) and 95% confidence interval for these five variables was 0.73 (0.63-0.79) in distinguishing those with and without preclinical AD via amyloid imaging. When age, gender, race, and education were added, the model improved: 0.80 (0.72-0.88). Finally, when apolipoprotein ε4 allele (APOε4), obtained via blood or saliva, was added to the model, accuracy improved: 0.84 (0.77-0.89). Similar results were found using CSF biomarker tau/Aβ42: AUCs (95% CI) were 0.68 (0.58-0.79) for driving variables alone, 0.77 (0.69-0.86) for driving variables and demographics, and 0.87 (0.80-0.94) driving variables, demographics, and apolipoprotein ε4 allele. These promising findings suggest that naturalistic driving behavior can predict those with and without preclinical AD. The AUC is further improved with demographics and APOε4, an easily obtainable genetic biomarker. This model may be used in clinical/research settings as a screen or adjunct for diagnostics and prognostics purposes.

scholarly journals GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

2019 ◽  
Vol 8 (5) ◽  
pp. 226 ◽  
Author(s):  
José Balsa-Barreiro ◽  
Pedro M. Valero-Mora ◽  
José L. Berné-Valero ◽  
Fco-Alberto Varela-García
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Driving Behavior ◽  
Geographic Information ◽  
Limited Resources ◽  
Gis Mapping ◽  
Research Groups ◽  
Naturalistic Driving ◽  
Gis Software ◽  
Operational Capacity

Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software.

Crash and Near-Crash Risk Assessment of Distracted Driving and Engagement in Secondary Tasks: A Naturalistic Driving Study

2018 ◽  
Vol 2672 (38) ◽  
pp. 245-254 ◽  
Author(s):  
Peter R. Bakhit ◽  
BeiBei Guo ◽  
Sherif Ishak
Keyword(s):  
Traffic Safety ◽  
Real World ◽  
Secondary Task ◽  
Safety Concern ◽  
Distracted Driving ◽  
Crash Risk ◽  
Secondary Tasks ◽  
Depth Analysis ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.

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