scholarly journals Naturalistic Study of Truck Following Behavior

2017 ◽  
Vol 2615 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Emily Nodine ◽  
Andy Lam ◽  
Mikio Yanagisawa ◽  
Wassim Najm
Keyword(s):  
Response Times ◽  
Truck Drivers ◽  
Crash Risk ◽  
Naturalistic Study ◽  
Manual Response ◽  
Baseline Case ◽  
Heavy Truck ◽  
Naturalistic Driving ◽  
Reduce Emissions ◽  
Automated Response

A baseline case was created for the following behavior of heavy-truck drivers with the use of naturalistic driving data to support the development of automated platooning. A truck platoon is a string of trucks following each other in the same lane at short distances. Grouping vehicles in platoons can increase capacity on roads, save significant fuel, reduce emissions, and potentially result in improved safety. However, these benefits can be realized only if the platoons operate in an automated, coordinated manner. Because little literature of truck following behavior exists to support the development of such truck platoons, this research focused on how closely trucks follow other vehicles on highways under various environmental conditions, how closely a truck follows a leading vehicle when other vehicles cut in between, and the safety impact of following at different headways. Findings indicate that trucks follow other vehicles at an average headway of about 2 s overall, and those headways are shorter when following a passenger car rather than a heavy truck, on state highways rather than on Interstates, in clear weather rather than in rain or snow, and during the day rather than during at night. Vehicles usually do not cut in when a truck is following another vehicle at less than 25-m (82-ft) or 1.0-s headway. For manual response times, the rear-end crash risk increases considerably at headways of less than 1.0 s; for automated response times, crash risk is almost negligible at headways as low as 0.5 s.

Cell Phone Conversation and Relative Crash Risk Update

2018 ◽  
pp. 5992-6006 ◽  
Author(s):  
Richard A. Young
Keyword(s):  
Protective Effect ◽  
Cell Phone ◽  
Response Times ◽  
Meta Analysis ◽  
Self Regulation ◽  
Study Data ◽  
Crash Risk ◽  
Recent Experimental Study ◽  
A Cell ◽  
Naturalistic Driving

This chapter reviews key findings since 2014 that are relevant to estimating the relative crash risk of conversing via a cell phone during real-world and naturalistic driving in passenger vehicles. It updates chapter 102 in the previous edition of this Encyclopedia (Young, 2015a). The objective is to determine if recent data confirms the conclusion in Young (2015a) that engaging in a cell phone conversation does not increase crash risk beyond that of driving without engaging in a cell phone conversation. In particular, a recent estimate is presented of the relative crash risk for cell phone conversation in the Strategic Highway Research Program 2 (SHRP2) naturalistic driving study data. This estimate is compared with five other estimates in a meta-analysis, which shows that cell phone conversation reduces crash risk (i.e., has a protective effect). A recent experimental study will also be discussed, which supports the hypothesis that driver self-regulation gives rise to the protective effect by compensating for the slight delays in event response times during cell phone conversation.

Cell Phone Conversation and Relative Crash Risk Update

2019 ◽  
pp. 775-790
Author(s):  
Richard A. Young
Keyword(s):  
Protective Effect ◽  
Cell Phone ◽  
Response Times ◽  
Meta Analysis ◽  
Self Regulation ◽  
Study Data ◽  
Crash Risk ◽  
Recent Experimental Study ◽  
A Cell ◽  
Naturalistic Driving

This chapter reviews key findings since 2014 that are relevant to estimating the relative crash risk of conversing via a cell phone during real-world and naturalistic driving in passenger vehicles. It updates Chapter 102 in the previous edition of this Encyclopedia. The objective is to determine if recent data confirms the conclusion that engaging in a cell phone conversation does not increase crash risk beyond that of driving without engaging in a cell phone conversation. In particular, a recent estimate is presented of the relative crash risk for cell phone conversation in the strategic highway research program 2 (SHRP2) naturalistic driving study data. This estimate is compared with five other estimates in a meta-analysis, which shows that cell phone conversation reduces crash risk (i.e., has a protective effect). A recent experimental study will also be discussed, which supports the hypothesis that driver self-regulation gives rise to the protective effect by compensating for the slight delays in event response times during cell phone conversation.

scholarly journals Response Time Distribution Analysis of Semantic and Response Interference in a Manual Response Stroop Task

2019 ◽  
Vol 66 (3) ◽  
pp. 231-238 ◽  
Author(s):  
Nabil Hasshim ◽  
Michelle Downes ◽  
Sarah Bate ◽  
Benjamin A. Parris
Keyword(s):  
Response Time ◽  
Stroop Task ◽  
Time Distribution ◽  
Response Times ◽  
Response Time Distribution ◽  
Stroop Interference ◽  
Response Conflict ◽  
Distribution Analysis ◽  
Manual Response ◽  
Response Interference

Abstract. Previous analyses of response time distributions have shown that the Stroop effect is observed in the mode (μ) and standard deviation (σ) of the normal part of the distribution, as well as its tail (τ). Specifically, interference related to semantic and response processes has been suggested to specifically affect the mode and tail, respectively. However, only one study in the literature has directly manipulated semantic interference, and none manipulating response interference. The present research aims to address this gap by manipulating both semantic and response interference in a manual response Stroop task, and examining how these components of Stroop interference affect the response time distribution. Ex-Gaussian analysis showed both semantic and response conflict to only affect τ. Analyzing the distribution by rank-ordered response times (Vincentizing) showed converging results as the magnitude of both semantic and response conflict increased with slower response times. Additionally, response conflict appeared earlier on the distribution compared to semantic conflict. These findings further highlight the difficulty in attributing specific psychological processes to different parameters (i.e., μ, σ, and τ). The effect of different response modalities on the makeup of Stroop interference is also discussed.

The Relationship between Abnormalities of Saccadic and Manual Response Times in Parkinson's Disease

2013 ◽  
Vol 3 (4) ◽  
pp. 557-563 ◽  
Author(s):  
Chrystalina A. Antoniades ◽  
Zheyu Xu ◽  
R.H.S. Carpenter ◽  
Roger A. Barker
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Response Times ◽  
Manual Response ◽  
The Relationship

scholarly journals Association of Photopic and Mesopic Contrast Sensitivity in older drivers with risk of motor vehicle collision using naturalistic driving data

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Cynthia Owsley ◽  
Thomas Swain ◽  
Rong Liu ◽  
Gerald McGwin ◽  
Mi Young Kwon
Keyword(s):  
Older Adults ◽  
Contrast Sensitivity ◽  
Motor Vehicle ◽  
Age Groups ◽  
Time Of Day ◽  
Motor Vehicle Crash ◽  
Older Drivers ◽  
Crash Risk ◽  
Sensitivity Testing ◽  
Naturalistic Driving

Abstract Background Older drivers have a crash rate nearly equal to that of young drivers whose crash rate is the highest among all age groups. Contrast sensitivity impairment is common in older adults. The purpose of this study is to examine whether parameters from the photopic and mesopic contrast sensitivity functions (CSF) are associated with incident motor vehicle crash involvement by older drivers. Methods This study utilized data from older drivers (ages ≥60 years) who participated in the Strategic Highway Research Program Naturalistic Driving Study, a prospective, population-based study. At baseline participants underwent photopic and mesopic contrast sensitivity testing for targets from 1.5–18 cycles per degree. Model fitting generated area under the log CSF (AULCSF) and peak log sensitivity. Participant vehicles were instrumented with sensors that captured continuous driving data when the vehicle was operating (accelerometers, global positioning system, forward radar, 4-channel video). They participated for 1–2 years. Crashes were coded from the video and other data streams by trained analysts. Results The photopic analysis was based on 844 drivers, and the mesopic on 854 drivers. Photopic AULCSF and peak log contrast sensitivity were not associated with crash rate, whether defined as all crashes or at-fault crashes only (all p > 0.05). Mesopic AULCSF and peak log sensitivity were associated with an increased crash rate when considered for all crashes (rate ratio (RR): 1.36, 95% CI: 1.06–1.72; RR: 1.28, 95% CI: 1.01–1.63, respectively) and at-fault crashes only (RR: 1.50, 95% CI: 1.16–1.93; RR: 1.38, 95% CI: 1.07–1.78, respectively). Conclusions Results suggest that photopic contrast sensitivity testing may not help us understand future crash risk at the older-driver population level. Results highlight a previously unappreciated association between older adults’ mesopic contrast sensitivity deficits and crash involvement regardless of the time of day. Given the wide variability of light levels encountered in both day and night driving, mesopic vision tests, with their reliance on both cone and rod vision, may be a more comprehensive assessment of the visual system’s ability to process the roadway environment.

Behavioral Analysis of Drivers Following Winter Maintenance Trucks Enabled with Collision Avoidance System

2019 ◽  
Vol 2673 (10) ◽  
pp. 394-404
Author(s):  
Rajat Verma ◽  
Ramin Saedi ◽  
Ali Zockaie ◽  
Timothy J. Gates
Keyword(s):  
Collision Avoidance ◽  
Traffic Safety ◽  
Response Times ◽  
Regression Tree ◽  
Field Testing ◽  
Operating Conditions ◽  
Classification And Regression Tree ◽  
Crash Risk ◽  
Winter Maintenance ◽  
Warning Light

Winter maintenance trucks (WMTs) often operate at lower speeds during inclement weather and roadway conditions, creating potential safety issues for motorists following close behind. In this study, a new prototype radar-based rear-end collision avoidance and mitigation system (CAMS) was tested to assess its impact on the behavior of drivers following WMTs. The system is designed to flash an auxiliary rear-facing warning light upon detection of a vehicle encroaching within an unsafe relative headway with the rear of the WMT. A series of field evaluations was performed during actual winter maintenance operations to assess the effectiveness of the system compared with normal operating conditions (i.e., without the CAMS warning light) toward improving driver behavior related to rear-end crash risk. Specifically, two measures were assessed: (a) rate of vehicles encroaching beyond a safe time headway threshold to the rear of the WMT, and (b) the reaction–response time of drivers. Classification and regression tree models were created for identifying the relevant factors influential in determining the change in driver response. The results indicate that this warning light was effective in reducing the likelihood of the subject drivers crossing beyond a relative headway of 4.5 s. It was also effective in reducing the reaction and response times of the drivers by 0.83 and 0.55 s (36% and 20% reduction), respectively. Although the results were encouraging, additional field testing is recommended before conclusions are drawn regarding the traffic safety impacts of the system.

scholarly journals Risk Factors in Work Zone Safety Events: A Naturalistic Driving Study Analysis

2019 ◽  
Vol 2673 (1) ◽  
pp. 379-387 ◽  
Author(s):  
Nipjyoti Bharadwaj ◽  
Praveen Edara ◽  
Carlos Sun
Keyword(s):  
Risk Factors ◽  
Driver Behavior ◽  
Work Zone ◽  
Crash Risk ◽  
Work Zones ◽  
Binary Logistic Regression Model ◽  
Work Zone Safety ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
The Impact

Identification of crash risk factors and enhancing safety at work zones is a major priority for transportation agencies. There is a critical need for collecting comprehensive data related to work zone safety. The naturalistic driving study (NDS) data offers a rare opportunity for a first-hand view of crashes and near-crashes (CNC) that occur in and around work zones. NDS includes information related to driver behavior and various non-driving related tasks performed while driving. Thus, the impact of driver behavior on crash risk along with infrastructure and traffic variables can be assessed. This study: (1) investigated risk factors associated with safety critical events occurring in a work zone; (2) developed a binary logistic regression model to estimate crash risk in work zones; and (3) quantified risk for different factors using matched case-control design and odds ratios (OR). The predictive ability of the model was evaluated by developing receiver operating characteristic curves for training and validation datasets. The results indicate that performing a non-driving related secondary task for more than 6 seconds increases the CNC risk by 5.46 times. Driver inattention was found to be the most critical behavioral factor contributing to CNC risk with an odds ratio of 29.06. In addition, traffic conditions corresponding to Level of Service (LOS) D exhibited the highest level of CNC risk in work zones. This study represents one of the first efforts to closely examine work zone events in the Transportation Research Board’s second Strategic Highway Research Program (SHRP 2) NDS data to better understand factors contributing to increased crash risk in work zones.

scholarly journals An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

2020 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Haotian Cao ◽  
Zhenghao Zhang ◽  
Xiaolin Song ◽  
Hong Wang ◽  
Mingjun Li ◽  
...  
Keyword(s):  
Cell Phone ◽  
Large Scale ◽  
Population Attributable Risk ◽  
Demographic Characteristics ◽  
Driving Safety ◽  
Crash Risk ◽  
Lower Percentage ◽  
Content Type ◽  
Driving Experience ◽  
Naturalistic Driving

Purpose The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages. Design/methodology/approach A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research. The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage. The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics (age, driving experience or their combination) and the crash risk regarding cell phone engagements, as well as the correlation among the likelihood of the cell phone engagement during the driving, multiple driver demographic characteristics (gender, age and driving experience) and environment conditions. Findings Senior drivers face an extremely high crash risk when distracted by cell phone during driving, but they are not involved in crashes at a large scale. On the contrary, cell phone usages account for a far larger percentage of total crashes for young drivers. Similarly, experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving, and cell phone engagements are attributed to a lower percentage of total crashes for them. Furthermore, experienced, senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving. Originality/value The results provide support to guide countermeasures and vehicle design.

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