100-Car naturalistic driving study
| 100-Car naturalistic driving study | |
|---|---|
| General information | |
| Type: Naturalistic driving study | |
| Tested system/service: | |
| Countries: USA | ? test users |
| ? partners | 100 vehicles |
| Active from 01/2001 to 11/2002 | |
| Contact | |
| http://www.vtti.vt.edu/PDF/100-Car Fact-Sheet.pdf | |
| Vicki L. Neale | |
| vneale@vtti.vt.edu | |
| Virginia Tech Transportation Institute US | |
| Catalogue entries | |
| Data catalogue | Tools catalogue |
| Data sets used in this FOT: No data set is |
The following tools were used in this FOT: No tool is linked |
A key to the development of effective crash countermeasures is an understanding of pre-crash causal and contributing factors. This research effort was initiated to provide an unprecedented level of detail concerning driver performance, behaviour, environment, driving context and other factors that were associated with critical incidents, near crashes and crashes for 100 drivers across a period of one year.
The 100-Car Naturalistic Driving Study database contains many extreme cases of driving behaviour and performance, including severe fatigue, impairment, judgment error, risk taking, willingness to engage in secondary tasks, aggressive driving, and traffic violations. The data set includes approximately 2,000,000 vehicle miles, almost 43,000 hours of data, 241 primary and secondary drivers, 12 to 13 months of data collection for each vehicle, and data from a highly capable instrumentation system including five channels of video and vehicle kinematics. From the data, an “event” database was created, similar in classification structure to an epidemiological crash database, but with video and electronic driver and vehicle performance data. The events are crashes, near crashes and other “incidents.” Data was classified by pre-event maneuver, precipitating factor, event type, contributing factors, and the avoidance maneuver exhibited. Parameters such as vehicle speed, vehicle headway, time-to-collision, and driver reaction time are also recorded.
The “100 Car Naturalistic Driving Study” is a three-phased effort designed to meet three objectives: Phase I, Conduct Test Planning Activities; Phase II, Conduct a Field Test; and Phase III, Prepare for Large-Scale Field Data Collection Effort. The large-scale field data collection effort is Phase IV, which is not being conducted under the current contract. The objectives of Phases I, II, and III are accomplished through 30 tasks spanning a 34-month period.
Details of Field Operational Test
Start date and duration of FOT execution
Duration: 12 to 13 months
Geographical Coverage
Northern Virginia/Washington, DC metropolitan area
Objectives
A primary goal was to provide vital exposure and pre-crash data necessary for understanding causes of crashes, supporting the development and refinement of crash avoidance countermeasures, and estimating the potential of these countermeasures to reduce crashes and their consequences.
Results
The event database that was created during the 100-Car Study can be useful for a variety of purposes; for example, evaluation of risky driving behaviour and crash risk, calculation of relative risk of engaging in secondary tasks, and evaluation of driver response to lead vehicle brake lights. To facilitate this process, the initial event database will be made publicly accessible via the Internet. In addition, the initial event database can be expanded to address additional issues, since all of the video and electronic data for the entire study have been archived.
Using both the video and electronic sensor data, an “event” database, consisting of crash, near-crash and crash-relevant conflict events, was created. This database consists of almost 10,000 such events.
Lessons learned
Subject recruitment: Future studies should always have a small number of “reserve” participants who can be called on relatively short notice to replace any drivers removed from the study. Thus, participant recruitment and initial screening should continue beyond the placement of the desired number of vehicles on the road.
The study shows also the importance of the person or persons who are in direct contact with the participants and who serve as the interface between the participants and the organization performing the study. These employees should be well trained in working with participants and with the resolution of the unique issues that are likely to arise in a study of this length and magnitude.
DAS Installation: If a subcontractor is hired to perform installation, maintenance, or repairs, the selection process should carefully consider the capabilities of the contractor, their willingness to receive specialized training, and their typical level of customer service. In addition, strict guidelines as to who will be responsible for repair and payment for an installation problem, detailed instructions for installations, and explicit expectations for the installation timeframe are all critical.
It might also be useful to institute random inspections of recently instrumented vehicles to catch any systematic problems with the installation that require further training or information to the installer.
Hardware and Software maintenance: The single most important lesson learned regarding system maintenance for the 100-Car Study was to have a maintenance person permanently located within the northern Virginia area near the study vehicles.
Hardware: The 100-Car Study DAS had a unique remote tracking capability that allowed study personnel to determine, based on GPS coordinates, the location of a vehicle. This functionality was essential when data downloaders had to locate the vehicles. The system was also able to transmit limited amounts of data from the vehicle that could be used for fault detection. In some cases, the system also allowed for the remote completion of small repairs to the system, especially those that involved resetting particular pieces of equipment. This capability should be maintained or expanded in future systems, if feasible.
Despite substantial efforts to prevent it, several data acquisitions systems drained the batteries of the cars in which they were installed. Better sensors and more robust system shutdown algorithms can be created to address the majority of these issues, and should be implemented in the future.
Data downloading: The main difficulty with data downloading consisted of gaining access to vehicles. In a related issue, detailed logs had to be kept of the data downloads for each of the vehicles. This allowed the downloader to prioritize vehicles according to the amount of data not yet downloaded, thereby minimizing the risk of data lost due to a full hard drive.
Data analysis: The data analysis process for this amount of data proved to be challenging, time-intensive, and complex. The main lesson is to allow enough time for databases to be created.
Privacy: Important lessons were learned with regard to protecting the confidentiality of the drivers in the study. To protect the drivers in the event of a crash, it was deemed important to obtain a Certificate of Confidentiality from the National Institutes of Mental Health (NIMH).
The purpose of this certificate was to prevent the data collected in the study from being subpoenaed so that it could not be used against a subject in court. However, obtaining the certificate imposed a constraint on the study: administrators at NIMH felt that it was important to protect the confidentiality of anyone in the vehicle who could be recorded via video or audio recordings. Therefore, the choice was made to use camera placement and angles that would only collect data on the driver and to only have audio recording active when the driver activated the incident push button.
Main events
Financing
Summary, type of funding and budget
Sponsors:
- National Highway Traffic Safety Administration (NHTSA)
- Virginia Department of Transportation (VDOT)
- Virginia Transportation Research Council (VTRC)
- Virginia Tech (VT)
Cooperation partners and contact persons
- Public Authorities: National Highway Traffic Safety Administration (NHTSA), Virginia Department of Transportation (VDOT)
- Industry
- Vehicle Manufacturer:
- Supplier:
- Users:
- Universities:
- Research Institutes:
- Others (specify):
Main Contact persons
Dr. Vicki L. Neale (540) 231-1514 vneale@vtti.vt.edu Co- Principal Investigator Virginia Tech Transportation Institute
Thomas Dingus tdingus@vtti.vt.edu Director Virginia Tech Transportation Institute
Applications and equipment
Applications tested
None
The goal of the study was to collect data for understanding causes of crashes, supporting the development and refinement of crash avoidance countermeasures, and estimating the potential of these countermeasures to reduce crashes and their consequences.
Vehicle
100 vehicles (78 driver owned cars + 22 leased cars)
Equipment carried by test users
Infrastructure
none
Test equipment
The 100-Car Study instrumentation package was designed and developed in-house by the VTTI Center for Technology Development.
5 camera views were recorded in the instrumented vehicle: (1) forward, (2) driver’s face/left side of vehicle, (3) rear-facing, (4) over the driver’s shoulder capturing the driver’s hands and feet, the steering wheel, and the instrument panel, and (5) right side of vehicle.
Infrared lighting was used to illuminate the vehicle cab so that the driver’s face and hands could be viewed on camera during night time driving.
To collect the data from the experimental vehicles, “chase vehicles” were used to track the vehicle, go to the location, and download data. The chase vehicle drivers “called” the vehicle using a cellular telephone and laptop configuration. In-house software then displayed a map showing icons for the chase vehicle and experimental vehicle locations. The chase vehicle driver then drove to the location of the instrumented vehicle and downloaded the data from the experimental vehicle (downloading required a data transfer cable connected to an outlet near the rear license plate of the instrumented vehicle, which was connected to a data storage device).
Methodology
Pre-simulation / Piloting of the FOT
Method for the baseline
none
Techniques for measurement and data collection
All video on board the 100-Car Study data collection system was compressed using MPEG 1 compression. This allowed greater storage of video on board the vehicle hard drives and required less server space to store the raw video data. While the initial data stream was recording at 30 Hz, the compression algorithm reduced the actual number of unique frames to approximately 7.5 frames per second.
Server managers kept detailed logs of the data sent from the northern Virginia location and the data received in Blacksburg. These logs were periodically compared to ensure that no data were missing. In addition, backup copies of the data were maintained in various locations in order to minimize the risk of data loss.
Databases from the 100-Car study are available for public use on the Virginia Tech Transportation Institute website:
http://www.access.vtti.vt.edu/.
Recruitment goals and methods
One hundred drivers who commuted into or out of the Northern Virginia/Washington, DC metropolitan area were initially recruited as primary drivers to have their vehicles instrumented or receive a leased vehicle for this study.
109 primary drivers were used because some drivers dropped out of the study and others were replaced for various reasons. Altogether there were 241 total drivers (primary drivers plus secondary drivers).
Time of exposure per driver was 12 to 13 months.
A goal of this study was to maximize the potential to record crash and near-crash events through the selection of subjects with higher than average crash- or near-crash risk exposure. Exposure was manipulated through the selection of a larger sample of drivers below the age of 25, and by the selection of a sample that drove more than the average number of miles.
After a review of literature summarizing the driver factors that contribute to rear-end crashes, an ideal age and gender distribution was determined:
- Age 18-20 years: drivers = 18 males and 12 females
- Age 21-24 years: drivers = 18 males and 12 females
- Age 25-34 years: drivers = 6 males and 4 females
- Age 35-44 years: drivers = 6 males and 4 females
- Age 45-54 years: drivers = 6 males and 4 females
- Age 55-64 years: drivers = 6 males and 4 females
The project was successful in achieving the gender distribution goal (60 percent male and 40 percent female). However, the age group recruiting goals were not met. Only 34 percent of participants were under age 25, as opposed to the goal of 60 percent. This was primarily due to the difficulty in trying to recruit participants who drove many miles per year (primarily by commuting). Commuters tend to be older, and younger people tend not to drive as many miles. Those younger participants who were recruited were typically college students who commuted to campus from some distance away.
Methods for the liaison with the drivers during the FOT execution
The study shows also the importance of the person or persons who are in direct contact with the participants and who serve as the interface between the participants and the organization performing the study. These employees should be well trained in working with participants and with the resolution of the unique issues that are likely to arise in a study of this length and magnitude.
Methods for data analysis, evaluation, synthesis and conclusions
Several analysis were performed in order to answer to the following goals:
- Classify and quantify causal factors and dynamic scenarios involved in each conflict category
- Operationally define a near-crash using quantitative measures
- Characterization of driver inattention as it relates to incidents, near-crashes, and crashes
- Driver performance in instrumented vehicles over time
- Determine rear-end crash contributing factors and dynamic conditions
- Determine lane change contributing factors and dynamic conditions
- Determine the distribution of inattention for each rearend lead-vehicle scenario
- Characterize each of the 4 re scenarios in relation to heinrich’s triangle
- Evaluate the performance of the hardware, sensors, and data collection system used in phase ii
Sources of information
“The 100-Car Naturalistic Driving Study: A Descriptive Analysis of Light Vehicle-Heavy Vehicle Interactions from the Light Vehicle Driver's Perspective”
http://www.fmcsa.dot.gov/facts-research/research-technology/report/100-car-naturalistic-study/
Project Fact Sheet:
http://www.vtti.vt.edu/PDFs/100-Car_Fact-Sheet.pdf
