Discretionary Cut-In Driving Behavior Risk Assessment Based on Naturalistic Driving Data

2021 ◽  
pp. 2-13
Author(s):  
Hongbo Gao ◽  
Chuan Hu ◽  
Guotao Xie ◽  
Chao Han
Keyword(s):  
Risk Assessment ◽  
Driving Behavior ◽  
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 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

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.

Driving risk assessment using driving behavior data under continuous tunnel environment

2019 ◽  
Vol 20 (8) ◽  
pp. 807-812 ◽  
Author(s):  
Ying Yan ◽  
Youhua Dai ◽  
Xiaodong Li ◽  
Jinjun Tang ◽  
Zhongyin Guo
Keyword(s):  
Risk Assessment ◽  
Driving Behavior ◽  
Driving Risk

scholarly journals Variations in Naturalistic Driving Behavior and Visual Perception at the Entrances of Short, Medium, and Long Tunnels

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xinsha Fu ◽  
Shijian He ◽  
Jintao Du ◽  
Xiaofei Wang ◽  
Ting Ge
Keyword(s):  
Visual Perception ◽  
Traffic Accident ◽  
Driving Behavior ◽  
Accident Risk ◽  
Saccade Amplitude ◽  
Driver Performance ◽  
General Variation ◽  
Naturalistic Driving ◽  
Short Tunnel ◽  
Correlation Factors

Driver behavior and visual perception are very important factors in the management of traffic accident risk at tunnel entrances. This study was undertaken to analyze the differences in driving behavior and visual perception at the entrances of three types of tunnels, namely, short, medium-length, and long tunnels, under naturalistic driving conditions. Using three driving behavior indicators (speed, deceleration, and position) and two visual perception indicators (fixation and saccade), the driving performance of twenty drivers at six tunnels (two tunnels per condition) was comparatively analyzed. The results revealed that the speed maintained by the drivers prior to deceleration with braking under the short-tunnel condition was significantly larger than that under the medium- and long-tunnel conditions and that the drivers had a greater average and maximum deceleration rates under the short-tunnel condition. A similar general variation of driver visual perception appeared under the respective tunnel conditions, with the number of fixations gradually increasing and the maximum saccade amplitude gradually decreasing as the drivers approached the tunnel portal. However, the variation occurred approximately 60 m earlier under the short-tunnel condition than under the medium- and long-tunnel conditions. Interactive correlations between driving behavior and visual perception under the three conditions were established. The commencement of active deceleration was significantly associated (with correlation factors of 0.80, 0.77, and 0.79 under short-, medium-, and long-tunnel conditions, respectively) with the point at which the driver saccade amplitude fell below 10 degrees for more than 3 s. The results of this study add to the sum of knowledge of differential driver performance at the entrances of tunnels of different lengths.

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