scholarly journals Distraction “Hangover”: Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors

2021 ◽  
pp. 001872082110122
Author(s):  
Joseph Snider ◽  
Ryan J. Spence ◽  
Anne-Marie Engler ◽  
Ryan Moran ◽  
Sarah Hacker ◽  
...  
Keyword(s):  
Correct Identification ◽  
Simulated Driving ◽  
Car Following ◽  
Driving Behaviors ◽  
Peripheral Cues ◽  
Driving Risk ◽  
Driving Scenario ◽  
Smartphone Use ◽  
Cognitive Cost

Objective We measured how long distraction by a smartphone affects simulated driving behaviors after the tasks are completed (i.e., the distraction hangover). Background Most drivers know that smartphones distract. Trying to limit distraction, drivers can use hands-free devices, where they only briefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating and back to driving adds an underappreciated, potentially long period to the total distraction time. Method Ninety-seven 21- to 78-year-old individuals who self-identified as active drivers and smartphone users engaged in a simulated driving scenario that included smartphone distractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eye tracking. Results The participants’ lateral speed was larger than baseline for 15 s after the end of a voice distraction and for up to 25 s after a text distraction. Correct identification of peripheral cues dropped about 5% per decade of age, and participants from the 71+ age group missed seeing about 50% of peripheral cues within 4 s of the distraction. During distraction, coherence with the lead car in a following task dropped from 0.54 to 0.045, and seven participants rear-ended the lead car. Breadth of scanning contracted by 50% after distraction. Conclusion Simulated driving performance drops dramatically after smartphone distraction for all ages and for both voice and texting. Application Public education should include the dangers of any smartphone use during driving, including hands-free.

scholarly journals Driving Simulator Validity of Driving Behavior in Work Zones

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yanning Zhang ◽  
Zhongyin Guo ◽  
Zhi Sun
Keyword(s):  
Delay Time ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Controlled Environment ◽  
Analysis Method ◽  
Work Zones ◽  
Relative Validity ◽  
Car Following ◽  
Driving Behaviors ◽  
Driving Scenario

Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.

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 A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

Influence of Driving Behaviors on the Stability in Car Following

2019 ◽  
Vol 20 (3) ◽  
pp. 1081-1098 ◽  
Author(s):  
Yunpeng Wang ◽  
Junjie Zhang ◽  
Guangquan Lu
Keyword(s):  
Car Following ◽  
Driving Behaviors ◽  
The Stability

scholarly journals The Use of SEM and Light Microscopy for the Characterization of Hypericum foliosum Aerial Part as a Medicinal Plant

2008 ◽  
Vol 14 (S3) ◽  
pp. 107-108
Author(s):  
R. Serrano ◽  
P. Ferreira ◽  
E.T. Gomes ◽  
O. Silva
Keyword(s):  
Quality Control ◽  
Medicinal Plant ◽  
Light Microscopy ◽  
Plant Material ◽  
Aerial Part ◽  
Chemical Characterization ◽  
Correct Identification ◽  
Herbal Drugs ◽  
Herbal Drug

The first step in quality control of herbal drugs is ensuring the correct identification of the plant intended for use. The required analytical procedures (macroscopic characterization of the entire or fragmentized material, microscopic characterization after pulverization of the plant material and chemical characterization), are usually described on quality monographs reported in authoritarian texts such as the European 6th Pharmacopoeia. Further information related to the name of the each herbal drug, the herbal drug definition, purity tests and assay are also provided.

Analysis of longitudinal driving behaviors during car following situation by the driver's EEG using PARAFAC

2013 ◽  
Vol 46 (15) ◽  
pp. 415-422 ◽  
Author(s):  
Toshihito Ikenishi ◽  
Takayoshi Kamada ◽  
Masao Nagai
Keyword(s):  
Car Following ◽  
Driving Behaviors

scholarly journals Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Naikan Ding ◽  
Linsheng Lu ◽  
Nisha Jiao
Keyword(s):  
Visual Perception ◽  
Traffic Flow ◽  
Crash Risk ◽  
Perceptual Information ◽  
Specific Flow ◽  
Car Following ◽  
Driving Behaviors ◽  
Level Data ◽  
Spatial Transferability ◽  
Model Approach

Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

scholarly journals The Role of Cue Utilization during a Simulated Driving Scenario

2019 ◽  
Vol 63 (1) ◽  
pp. 848-852
Author(s):  
Daniel Sturman ◽  
Mark W. Wiggins
Keyword(s):  
Performance Metrics ◽  
Cerebral Oxygenation ◽  
Driving Performance ◽  
Cognitive Resources ◽  
Cue Utilization ◽  
Simulated Driving ◽  
Driving Scenario ◽  
Driving Task ◽  
And Performance ◽  
Simulated Driving Task

The present study was designed to establish whether a cue-based assessment of driving could predict cognitive load and performance during a simulated driving task. Following an assessment of cue utilization in the domain of driving, participants completed a moderate workload simulated driving task, during which cerebral oxygenation, eye behavior, and driving performance metrics were recorded. During the simulated driving task, participants with higher cue utilization recorded smaller increases in cerebral oxygenation in the prefrontal cortex relative to baseline, and smaller mean fixation dispersions, compared to participants with lower cue utilization. There were no statistically significant differences in the number of speed exceedances nor missed traffic signals based on cue utilization. These outcomes suggest that participants with higher cue utilization were able to allocate fewer cognitive resources to the simulated driving task, while maintaining an equivalent level of driving performance, compared to participants with lower cue utilization.

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