Motivation

Drowsiness is an intermediate condition that fluctuates between alertness and sleep. It reduces the consciousness level and hinders a person from responding quickly to important road safety issues [1]. The American Automobile Association (AAA) has reported that about 24% of 2,714 drivers that participated in a survey revealed being extremely drowsy while driving, at least once in the last month [2]. In 2017, the National Highway Transportation Safety Administration (NHTSA) also reported 795 fatalities in motor vehicle crashes involving drowsy drivers [3]. Drowsy driving has caused about 2.5% of fatal accidents from 2011 through 2015 in the USA, and it is estimated to produce an economic loss of USD 230 billion annually [4]. Klauer et al. have found in their study that drowsy drivers contributed to 22-24% of crashes or near-crash risks [5]. The German Road Safety Council (DVR) has reported that one out of four fatal highway crashes has been caused by drowsy drivers [6]. In a study carried out in 2015, it has been reported that the average prevalence of falling asleep while driving in the previous two years was about 17% in 19 European countries [6]. The results of these studies emphasize the importance of detecting drowsiness early enough to initiate preventive measures. Drowsiness detection systems are intended to warn the drivers before an upcoming level of drowsiness gets critical to prevent drowsiness-related accidents.

Intelligent Systems that automate motor vehicle driving on the roads are being introduced to the market step-wise. The Society of Automotive Engineers (SAE) issued a standard defining six levels ranging from no driving automation (level 0) to full driving automation (level 5) [7]. While the SAE levels 0-2 require that an attentive driver carries out or at least monitors the dynamic driving task, in the SAE level 3 of automated driving, drivers will be allowed to do a secondary task allowing the system to control the vehicle under limited conditions, e.g., on a motorway. Still, the automation system has to hand back the vehicle guidance to the driver whenever it cannot control the state of the vehicle any more. However, the handover of vehicle control to a drowsy driver is not safe. Therefore, the system should be informed about the state of the driver.

To date, different Advanced Driver Assistance Systems (ADAS) have been made by car manufactures and researchers to improve driving safety and manage the traffic flow. ADAS systems have been benefited from advanced machine perception methods, improved computing hardware systems, and intelligent vehicle control algorithms. By recently increasing the availability of huge amounts of sensor data to ADAS, data-driven approaches are extensively exploited to enhance their performance. The driver drowsiness detection systems have gained much attention from researchers. Before its use in the development of driving automation, drowsiness warning systems have been produced for the direct benefit of avoiding accidents.

The aim of the WACHSens project was to collect a big data set to detect the different levels of driver drowsiness during performing two different driving modes: manual and automated.

To retrieve this data set, please send a request to: arno.eichberger@tugraz.at

References:

[1] M. Awais, N. Badruddin, and M. Drieberg, "A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability,"Sensors, vol. 17, no. 9, 2017, doi: 10.3390/s17091991

[2] AAA Foundation for Traffic Safety, "2019 Traffic Safety Culture Index (Technical Report), June 2020," Washington, D.C., Jun. 2020. [Online]. Available: https://aaafoundation.org/2019-traffic-safety-culture-index/

[3] National Highway Traffic Safety Administration, "Traffic Safety Facts: 2017 Fatal Motor Vehicle Crashes: Overview," NHTSA's National Center for Statistics and Analysis, 1200 New Jersey Avenue SE., Washington DOT HS 812 603, Oct. 2018. Accessed: Apr. 14 2021. [Online]. Available: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812603

[4] Agustina Garcés Correa, Lorena Orosco, and Eric Laciar, "Automatic detection of drowsiness in EEG records based on multimodal analysis," Medical Engineering & Physics, vol. 36, no. 2, pp. 244–249, 2014, doi: 10.1016/j.medengphy.2013.07.011

[5] S. Klauer, V. Neale, T. Dingus, Jeremy Sudweeks, and D. J. Ramsey, "The Prevalence of Driver Fatigue in an Urban Driving Environment : Results from the 100-Car Naturalistic Driving Study," in 2006.

[6] Fraunhofer-Gesellschaft,Eyetracker warns against momentary driver drowsiness - Press Release Oktober 12, 2010. [Online]. Available: https://www.fraunhofer.de/en/press/research-news/2010/10/eye-tracker-driver-drowsiness.html (accessed: Apr. 14 2021).

[7] T. Inagaki and T. B. Sheridan, "A critique of the SAE conditional driving automation definition, and analyses of options for improvement," Cogn Tech Work, vol. 21, no. 4, pp. 569–578, 2019, doi: 10.1007/s10111-018-0471-5

Dataset Description

This dataset contains 10,387 subjective ratings of freeway driving experiences collected by means of a web survey completed (or partially completed) by 1,614 Brazilian drivers. Each participant watched up to 12 one-minute video clips, each depicting a freeway driving scenario from the driver viewpoint. A total of 418 video clips depicted 128 scenarios that were systematically varied across five key factors: number of lanes in the trip direction, speed limit, road grade, truck percentage, and traffic density, according to a fractional factorial design.

Each rating, on a scale of 0 to 100, represents the perceived quality of the driving experience, with higher scores indicating a more positive perception (from "poor" to "excellent" on a continuous scale). Each line in the dataset represents a rating and includes the following variables:

Scenario Encoding

The Video_clip_scenario variable uses a specific encoding scheme to represent the different driving conditions (scenarios). Each factor is represented by up to 3 letters and a numeric value. For example, NL3_G4_SL120_PT10_k132_veh1 indicates the first replication of scenario with 3 lanes, 4% uphill grade, a 120 km/h speed limit, 10% trucks, and a traffic density of 13.2 veh/km/lane. The factors and their numeric values are explained in the following table:

These ratings were obtained using a web survey, approved by the Brazilian Human Research Ethics Committee (CEP-EACH-USP, protocol number: 2034830). The ratings.csv file contains the unfiltered ratings obtained on that web survey.

For additional information about the dataset, contact the project leader or the data curator. If you find this dataset useful for your research, please let the authors know. We appreciate your feedback and would be interested to hear how you use the data.

It's bdd100k dataset from https://bdd-data.berkeley.edu/

About

https://bair.berkeley.edu/blog/2018/05/30/bdd/

ToolKit

https://github.com/ucbdrive/bdd-data

Licence

https://doc.bdd100k.com/license.html#license

Reference

[1] Huazhe Xu, Yang Gao, Fisher Yu, and Trevor Darrell. "End-to-end learning of driving models from large-scale video datasets." CVPR 2017 [2] Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, Trevor Darrell. "BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling" arXiv:1805.04687 [3] Ye Xia, Danqing Zhang, Jinkyu Kim, Ken Nakayama, Karl Zipser, David Whitney. "Predicting Driver Attention in Critical Situations" ACCV 2018

We offer a robust dataset of high-quality, real-time vehicle data sourced from a fleet of over 150,000 vehicles, delivering detailed insights into driving behavior, battery health, and charging patterns. Collected with 100% informed driver consent, our data is gathered through direct vehicle connections via APIs or installed hardware, ensuring transparency, privacy compliance, and unparalleled accuracy. This makes it a powerful resource for applications like usage-based insurance (UBI), telematics-qualified leads, and telematics-qualified rates.

Our dataset’s granularity and real-time capabilities set it apart, providing precise, up-to-date information that eliminates reliance on assumptions or aggregated sources. For usage-based insurance, it enables insurers to craft personalized premiums based on actual driving habits—such as mileage, speed, braking patterns, and time of day—reducing risk exposure and rewarding safer drivers. Telematics-qualified leads benefit from this data by identifying high-potential customers with driving profiles that align with insurer criteria, streamlining marketing efforts. For telematics-qualified rates, our data supports dynamic pricing models, allowing insurers to adjust rates with confidence using verified, real-time driving insights.

Beyond insurance, the dataset serves broader applications. Energy companies can leverage insights into charging patterns, battery life, and energy usage to optimize EV infrastructure. Autonomous vehicle developers can train AI models with real-world driving behavior and environmental data, improving system safety and performance. Fleet managers, urban planners, and transportation agencies also gain value from traffic pattern analysis, vehicle usage trends, and congestion data, supporting smarter infrastructure and sustainability initiatives.

With informed consent at its core, our data collection process fosters trust and meets privacy standards, while direct vehicle connections ensure actionable, reliable insights. This dataset empowers industries to enhance operational efficiency, refine customer targeting, and develop tailored, data-driven solutions like UBI, telematics-qualified leads, and telematics-qualified rates, driving innovation and value across the board.

About 228,200 Americans had a license to operate a motor vehicle in the United States in 2020. That year, an estimated 36,680 people died on U.S. roads. Traffic-related fatalities per 100,000 licensed drivers stood at 17.01 in 2020.

Road safety rankings
The United States has among the highest rates of road fatalities per population worldwide. Possible contributing factors to deaths on the road can include speeding, not wearing a seatbelt, driving while under the influence of drugs or alcohol, and driving while fatigued. Traffic fatalities caused by speeding in the United States have declined since 2008, with less than 10,000 deaths recorded annually over recent years.

Automation for the nation
94 percent of severe automobile crashes are due to human error — but driving safety is taken much more seriously today than in the past, with roughly 90 percent of U.S. drivers wearing their seatbelts while driving in 2020. Over recent years, car manufacturers and developers have striven to reduce car crashes even further with partially and fully automated safety features such as forward collision warnings, lane departure warnings, rearview video systems, and automatic emergency braking. Self-driving vehicles are also set to take to the roads in the future, with car brands such as Toyota, Ford, and GM registering over 350 autonomous driving patents respectively in the United States.

Qatar Consumer Price Index (CPI): Household Services: Servants and Drivers data was reported at 120.950 2001=100 in Dec 2008. This stayed constant from the previous number of 120.950 2001=100 for Sep 2008. Qatar Consumer Price Index (CPI): Household Services: Servants and Drivers data is updated quarterly, averaging 110.205 2001=100 from Mar 2005 (Median) to Dec 2008, with 16 observations. The data reached an all-time high of 120.950 2001=100 in Dec 2008 and a record low of 101.000 2001=100 in Jun 2005. Qatar Consumer Price Index (CPI): Household Services: Servants and Drivers data remains active status in CEIC and is reported by Ministry of Development Planning and Statistics . The data is categorized under Global Database’s Qatar – Table QA.I008: Consumer Price Index: 2001=100.

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The self-driving taxi market has the potential to grow by 56170.00 units during 2021-2025, and the market’s growth momentum will accelerate at a CAGR of 58.54%.

This self-driving taxi market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentation by level of autonomy (SAE level 3 and SAE level 4 and 5) and geography (North America, Europe, APAC, South America, and MEA). The self-driving taxi market report also offers information on several market vendors, including Alphabet Inc., Aurora Operations Inc., Ford Motor Co., General Motors Co., Renault SA, Stellantis NV, Tesla Inc., Toyota Motor Corp., Volkswagen AG, and Volvo Car Corp. among others.

What will the Self-driving Taxi Market Size be in 2021?

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Self-driving Taxi Market: Key Drivers and Trends

Based on our research output, there has been a negative impact on the market growth during and post COVID-19 era. The increased focus of OEMs toward the development of self-driving vehicles is notably driving the self-driving taxi market growth, although factors such as issues with system reliability may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the self-driving taxi industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

This self-driving taxi market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Self-driving Taxi Market Vendors

BCIT Baseline Driving

Introduction

Overview: The Baseline Driving study was designed to collect extended time-on-task measurements of subjects performing a driving task in a simulated environment in order to assess fatigue-based performance through novel biomarkers. The Baseline Driving study was intended to identify periods of driver fatigue via predictive algorithms formulated from the analysis of driver EEG data, in comparison to the objective performance measures, and in contrast with the (non-fatigued) Calibration driving session for the subject.

Baseline driving data sets were designed to be the second component of every recording session within the BCIT program, which featured multiple studies investigating fatigue.

Collectively, the Baseline Driving recordings comprise a virtual study, in which long time-on-task driving performance can be analyzed for fatigue-related EEG biomarkers based on measured driving performance degradation. Further information is available on request from cancta.net.

The task was performed using identical systems at three different sites:

• Army Research Laboratory, Aberdeen MD (T1)
• Teledyne Corporation, Durham, NC (T2)
• Science Applications International Corporation (SAIC), Louisville, CO (T3)

All sites used identical driving simulator setups. The data collected at site T1 used a 64-channel Biosemi EEG headset as did the data collected at site T2, while site T3 used a 256-channel Biosemi EEG headset.

Data from site T1 has legacy subject IDs in the range 1000 to 1999. Data from site T2 has legacy subject IDs in the range 2000 to 2999. Data from site T3 has legacy subject IDs in the range 3000 to 3999. Legacy subject IDs are unique across the entire BCIT program.

Methods

Subjects: Subjects at Aberdeen Proving Grounds were recruited, on a voluntary basis from among the scientists and engineers working at APG.

Subjects recruited by Teledyne and SAIC were found via advertising and community outreach efforts, and primarily consisted of local college students.

Apparatus: Driving simulator with steering wheel and brake / foot pedals (Real Time Technologies; Dearborn, MI); Video Refresh Rate (VRR) = 900 Hz; Vehicle data log file Sampling Rate (SR) = 100 Hz); EEG (BioSemi 256 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=1024 Hz); Eye Tracking (Sensomotoric Instruments (SMI); REDEYE250). Eye tracking data is not included in this dataset.

Initial setup: Upon arrival to the lab, subjects were given an introduction to the primary study for which they were recruited and provided informed consent and provided demographics information. This was followed by a practice session, to acclimate the subject to the driving simulator. The driving practice task lasted 10-15 min, until asymptotic performance in steering and speed control was demonstrated and lack of motion sickness was reported. Subjects were then outfitted and prepped for eye tracking and EEG acquisition.

Task organization: Subjects always began recording sessions by performing a Calibration Driving task, which was a 15-minute drive where the subject controlled only the steering (and speed was controlled by the simulator).

Following this, subjects would perform the Baseline Driving task and the Guard Duty task, with counter-balancing used across subjects as to which of them came first.

The Baseline Driving run was 60 minutes of driving, performed in 6 blocks of 10 minutes each, with subjects responsible for speed and steering control.

The subject was instructed to stay within the boundaries of the right-most lane, and to drive at the posted speed limits. The vehicle was periodically subject to lateral perturbing forces, which could be applied to either side of the vehicle, pushing the vehicle out of the center of the lane; and the subject was instructed to execute corrective steering actions to return the vehicle to the center of the lane.

Independent variables: For T1 (ARL) and T3 (SAIC) there were no independent variables. For T2 data sets (Teledyne), independent variables were Visual Complexity (high vs. low), Perturbation Frequency (high vs. low).

Dependent variables: Reaction times to perturbations, continuous performance based on vehicle log (steering wheel angle, lane position, heading error, etc.), Task-Induced Fatigue Scale (TIFS), Karolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F). Note: questionnaire data is available upon request from cancta.net.

Additional data acquired: Participant Enrollment Questionnaire, Subject Questionnaire for Current Session, Simulator Sickness Questionnaire.

Experimental Locations: Army Research Laboratory, Aberdeen MD (site T1); Teledyne Corporation, Durham, NC (site T2); Science Applications International Corporation (SAIC), Louisville, CO (site T3).

Note 1: This dataset has a corresponding dataset in the BCIT Calibration Driving ds004118 which has the 15 minute driving task performed prior to this one.

Note 2: Some of the subjects in this dataset performed either the BCIT Basic Guard Duty Task (ds004118) or the BCIT Advanced Guard Duty Task (ds004106) counterbalanced during the same session.

The zip file contains the map used for the statistics calculated in the publication 'A Continental Assessment of the Drivers of Tropical Deforestation With a Focus on Protected Areas', available at https://doi.org/10.3389/fcosc.2022.830248.

This is a first version of the drivers of forest loss map, It is an allocation of crowdsourced data on predominant drivers of forest loss (data available here https://doi.org/10.1038/s41597-022-01227-3) to the Hansen et al (2013) dataset of global tree loss (v 1.7), set to a resolution of 100x100 m.

Furthermore, the classes 'Other subsistence agriculture' and 'Shifting cultivation' are parts of the 'Subsistence agriculture' class on the original crowdsourced data, separated by extracting shifting agriculture according to Heinimann et al (2017).

The map is in Goode Homolosine projection.

IntroductionWhile driving, drivers frequently adapt their driving behaviors according to their perception of the road’s alignment features. However, traditional two-dimensional alignment methods lack the ability to capture these features from the driver’s perspective.MethodThis study introduces a novel method for road alignment recognition, employing image recognition technology to extract alignment perspective features, namely alignment perspective skewness (APS) and alignment perspective kurtosis (APK), from in-real driving images. Subsequently, the K-means clustering algorithm is utilized for road segment classification based on APS and APK indicators. Various sliding step length for clustering are employed, with step length ranging from 100m to 400m. Furthermore, the accident rates for different segment clusters are analyzed to explore the relationship between alignment perspective features and traffic safety. A 150 km mountain road section of the Erlianhaote-Guangzhou freewway from Huaiji to Sihui is selected as a case study.ResultsThe results demonstrate that using alignment perspective features as classification criteria produces favorable clustering outcomes, with superior clustering performance achieved using shorter segment lengths and fewer cluster centers. The road segment classification based on alignment perspective features reveals notable differences in accident rates across categories; while traditional two-dimensional parameters-based classification methods fail to capture these differences. The most significant differences in accident rates across categories are observed with segment length of 100m, with the significance gradually diminishing as segment length increases and disappearing entirely when the length exceeds 300m.ImplicationThese findings validate the reliability of using alignment perspective features (APS and APK) for road alignment classification and road safety analysis, providing valuable insights for road safety management.

Emergency LED Drivers Market size was valued at USD 5.2 Billion in 2024 and is projected to reach USD 11.8 Billion by 2032, growing at a CAGR of 10.97% during the forecast period 2026-2032.

Emergency LED Drivers Market Drivers

Globally, building codes and safety regulations mandate emergency lighting systems in commercial, industrial, and public buildings to ensure safe evacuation during power outages or emergencies like fires. Emergency LED drivers are crucial components in these systems, providing the necessary backup power to LED fixtures.

The widespread shift towards energy-efficient LED lighting across various sectors (commercial, residential, industrial, automotive) is directly driving the demand for compatible emergency backup solutions. As LED lighting becomes the standard, the need for emergency LED drivers to support these installations grows.

Increased awareness among building owners, facility managers, and occupants regarding safety and security during emergency situations is a significant driver. Reliable emergency lighting powered by LED drivers ensures visibility and facilitates safe egress.

The growth in construction activities, particularly in developing economies, necessitates the installation of emergency lighting systems in new buildings, thereby driving the demand for emergency LED drivers.

IntroductionWhile driving, drivers frequently adapt their driving behaviors according to their perception of the road’s alignment features. However, traditional two-dimensional alignment methods lack the ability to capture these features from the driver’s perspective.MethodThis study introduces a novel method for road alignment recognition, employing image recognition technology to extract alignment perspective features, namely alignment perspective skewness (APS) and alignment perspective kurtosis (APK), from in-real driving images. Subsequently, the K-means clustering algorithm is utilized for road segment classification based on APS and APK indicators. Various sliding step length for clustering are employed, with step length ranging from 100m to 400m. Furthermore, the accident rates for different segment clusters are analyzed to explore the relationship between alignment perspective features and traffic safety. A 150 km mountain road section of the Erlianhaote-Guangzhou freewway from Huaiji to Sihui is selected as a case study.ResultsThe results demonstrate that using alignment perspective features as classification criteria produces favorable clustering outcomes, with superior clustering performance achieved using shorter segment lengths and fewer cluster centers. The road segment classification based on alignment perspective features reveals notable differences in accident rates across categories; while traditional two-dimensional parameters-based classification methods fail to capture these differences. The most significant differences in accident rates across categories are observed with segment length of 100m, with the significance gradually diminishing as segment length increases and disappearing entirely when the length exceeds 300m.ImplicationThese findings validate the reliability of using alignment perspective features (APS and APK) for road alignment classification and road safety analysis, providing valuable insights for road safety management.

China IQI: MoM: HS4: Motor Vehicles for the Transport of Ten or More Persons, Including the Driver. data was reported at 40.600 Average 12 Mths PY=100 in Feb 2025. This records a decrease from the previous number of 85.600 Average 12 Mths PY=100 for Jan 2025. China IQI: MoM: HS4: Motor Vehicles for the Transport of Ten or More Persons, Including the Driver. data is updated monthly, averaging 82.900 Average 12 Mths PY=100 from Jan 2018 (Median) to Feb 2025, with 79 observations. The data reached an all-time high of 813.800 Average 12 Mths PY=100 in Sep 2020 and a record low of 1.289 Average 12 Mths PY=100 in Jan 2023. China IQI: MoM: HS4: Motor Vehicles for the Transport of Ten or More Persons, Including the Driver. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: HS4 Classification.

ObjectiveDriving under the influence of alcohol and/or drugs impairs skills essential for safe driving, increases the risk of being involved in a traffic accident and is particularly prevalent in Spain. The aim is to assess the prevalence of positive substance driving cases, what factors may be associated with driving after substance use, and the evolution of the progress in the prevalence of drug use among drivers in drivers based on the 2008, 2013, 2018, and 2021 studies.Study design and settingThe present study was conducted in a representative sample of Spanish drivers in 2021 for alcohol (breath) and psychoactive substances [oral fluid (OF)]. The sample size was 2980 drivers, mostly males (76.5%) with a mean age of 41.35 ± 13.34 years.ResultsIn 2021, 9.3% of drivers tested positive for alcohol and/or drugs. The presence of alcohol alone was observed in 4.2% of drivers, alcohol and another substance in 0.3%, a single drug in 4.4%, and two or drugs other than alcohol in 0.4%. Overall, cocaine cases were the highest registered in 2021 (2.4%), while cannabis (1.9%) and polydrug cases (0.7%) were the lowest, with respect to the 2008/2013/2018 studies.ConclusionsAccording to our research, in 2021, 9 out of 100 drivers were detected to have some substance in their system. This prevalence remains unacceptably high in Spain, with a marked increase in the frequency of driving after cocaine use. Further interventions and measures must be taken to avoid driving under the influence of alcohol and/or drugs.

Importance: Handheld phone use while driving is a major factor in vehicle crashes. Scalable interventions are needed to encourage drivers to put down their phones.Objective: Test whether interventions involving social comparison feedback and/or incentives can reduce drivers’ handheld phone use.Design: Randomized controlled trial.Setting: Interventions were administered nationwide in the U.S. via a mobile application in the context of a usage-based insurance program.Participants: Progressive Insurance customers were eligible if enrolled in the Snapshot Mobile program for 30-70 days. 17,663 customers were invited by email to participate, and 2,109 opted in and were randomized.Interventions: Participants were randomly assigned to one of six trial arms for a 7-week intervention period: 1) control; 2) feedback, weekly push notification about how their handheld phone use compared to that of similar others; 3) standard incentive, max $50 award at the end of the intervention based on how their handheld use compared to similar others’; 4) standard incentive + feedback, combining interventions of arms 2 and 3; 5) reframed incentive + feedback, max $7.15 award each week, framed asparticipant’s to lose; 6) doubled reframed incentive + feedback, max $14.29 weekly loss-framed award.Intervention dates: May 13-June 30, 2019.Main Outcome and Measure: 100 Proportion of drive time engaged in handheld phone use in seconds/hour (s/h) of driving.Results: Analysis completed December 22, 2023. 2,020 drivers finished the intervention period (68% female; median (IQR) age 30 (25, 39). Median (IQR) baseline handheld phone use was 216 (72, 480) s/h. Relative to control, feedback and standard incentive participants did not reduce their handheld phone use. Standard incentive + feedback participants reduced their use by −38 (95% CI: −69 to −8) s/h, P = .045; reframed incentive + feedback participants reduced their use by −56 (95% CI: −87 to −26) s/h, P < .001; and doubled reframed incentive + feedback participants reduced their use by −42 s/h (95% CI: −72 to −13), P = .007. The five active treatment arms did not differ significantly from each other.Conclusions and Relevance: In this randomized trial, providing social comparison feedback plus incentives reduced handheld phone use while driving.Requests for a deidentified dataset and data dictionary can be made to the corresponding author at mucio.delgado@pennmedicine.upenn.edu. The data will be made available with investigator support to researchers whose proposed use of the data has been approved by the authors.

BCIT Calibration Driving

Introduction

Overview: The Calibration Driving study was intended to provide calibration data for applying fatigue-based driver performance prediction algorithms. Calibration data sets were designed to be the first component of every recording session within the BCIT program, which featured multiple studies investigating fatigue.

Collectively, the Calibration Driving recordings comprise a 'virtual' study, in which driving performance at the calibration level can be analyzed. When analyzed with other same-subject data, involving much longer tasks, the calibration data sets can be used as the basis for non-fatigue state performance.

Further information is available on request from cancta.net.

The task was performed using identical systems at three different sites:

• Army Research Laboratory, Aberdeen MD (T1)
• Teledyne Corporation, Durham, NC (T2)
• Science Applications International Corporation (SAIC), Louisville, CO (T3)

All sites used identical driving simulator setups.

The data collected at site T1 used a 64-channel Biosemi EEG headset as did the data collected at site T2, while site T3 used a 256-channel Biosemi EEG headset.

Data from site T1 has legacy subject IDs in the range 1000 to 1999. Data from site T2 has legacy subject IDs in the range 2000 to 2999. Data from site T3 has legacy subject IDs in the range 3000 to 3999. Legacy subject IDs are unique across the entire BCIT program.

Methods

Subjects: Subjects at Aberdeen Proving Grounds were recruited, on a voluntary basis from among the scientists and engineers working at APG.

Subjects recruited by Teledyne and SAIC were found via advertising and community outreach efforts, and primarily consisted of local college students.

Apparatus: Driving simulator with steering wheel and brake / foot pedals (Real Time Technologies; Dearborn, MI); Video Refresh Rate (VRR) = 900 Hz; Vehicle data log file Sampling Rate (SR) = 100 Hz); EEG (BioSemi 256 (+8) channel systems with 4 eye and 2 mastoid channels recorded; SR=1024 Hz); Eye Tracking (Sensomotoric Instruments (SMI); REDEYE250).

Initial setup: Upon arrival to the lab, subjects were given an introduction to the primary study for which they were recruited and provided informed consent and provided demographics information. This was followed by a practice session, to acclimate the subject to the driving simulator. The driving practice task lasted 10-15 min, until asymptotic performance in steering and speed control was demonstrated and lack of motion sickness was reported.

Subjects were then outfitted and prepped for eye tracking and EEG acquisition.

Task organization: The Calibration study featured a 15-minute trial, requiring the driver to control the steering of a simulated vehicle on a long, straight road in a visually sparse environment.

With the vehicle speed controlled by the driving simulator, the only task for the subject was to maintain the vehicle position in the center of the lane. The vehicle was periodically subject to lateral perturbing forces, which could be applied to either side of the vehicle, pushing the vehicle out of the center of the lane; and the subject was instructed to execute corrective steering actions to return the vehicle to the center of the lane.

Independent variables: None.

Dependent variables: Reaction times to perturbations, continuous performance based on vehicle log (steering wheel angle, lane position, heading error, etc.), Task-Induced Fatigue Scale (TIFS), Karolinska Sleepiness Scale (KSS), Visual Analog Scale of Fatigue (VAS-F).

Note: questionnaire data is available upon request from cancta.net.

Additional data acquired: Participant Enrollment Questionnaire, Subject Questionnaire for Current Session, Simulator Sickness Questionnaire.

Experimental Locations: Army Research Laboratory, Aberdeen MD (site T1); Teledyne Corporation, Durham, NC (site T2); Science Applications International Corporation (SAIC), Louisville, CO (site T3).