Naturalistic Driving Study Database Research Articles

How are drivers’ stress levels and emotions associated with the driving context? A naturalistic study

IntroductionUnderstanding and mitigating drivers' negative emotions, and stress levels, is of high importance for enhancing road safety, and human well-being. While detecting drivers' stress and negative emotions can significantly help with this goal, understanding what might be associated with increases in drivers' negative emotions and high stress level, might better help with planning interventions, which has not been explored in detail. Methods: In this study, by using a naturalistic driving study database, we analyze the changes in drivers' heart rate and facial expressions with respect to the changes in the driving scene, including road objects and the dynamical relationship between the ego vehicle and the lead vehicle. Results: Our results indicate that different road objects might be associated with varying levels of increase in drivers' HR as well as different proportions of negative facial emotions detected through computer vision. Larger vehicles on the road, such as trucks and buses, are associated with the highest amount of increase in drivers' HR as well as negative emotions. Additionally, shorter distances and higher standard deviation in the distance to the lead vehicle are associated with a higher number of abrupt increases in drivers’ HR, depicting a possible increase in stress level. Our finding indicates more positive emotions, lower facial engagement, and a lower abrupt increase in HR in highway environments. Conclusion: This research collectively shows that certain road environments such as highways with less stress-inducing objects are much more suited to promote drivers well-being and should be considered in routing use cases.

Cluster analysis and multi-level modeling for evaluating the impact of rain on aggressive lane-changing characteristics utilizing naturalistic driving data

This study investigated lane-changing characteristics with regard to drivers’ aggressiveness in rain and clear weather utilizing the SHRP2 Naturalistic Driving Study (NDS) dataset. An innovative methodology was developed to identify lane-changing events and extract corresponding parameters from the SHRP2 NDS database. Initially, K-means and K-medoids clustering methods were examined to classify drivers into non-aggressive and aggressive categories considering six features related to driving behavior, and K-means clustering was adopted based on the average silhouette width method (ASWM). Two-level mixed-effects linear regression models were calibrated to assess the contributing factors that affect lane-changing durations, which revealed that different vehicle kinematics, traffic, driver, and roadway characteristics, as well as weather conditions combined with other factors, were significant in the calibrated models for both driver types. The results revealed that the lane-changing duration associated with heavy rain decreased with a higher speed limit for aggressive drivers. Furthermore, the lane-changing duration associated with light/moderate rain decreased with the number of lanes for non-aggressive drivers. The study findings could be leveraged to incorporate drivers’ aggressiveness into microsimulation lane-changing model calibration and validation as well as could have significant implications in improving safety in Connected and Autonomous Vehicles (CAV).

Analysis of Headway and Speed Based on Driver Characteristics and Work Zone Configurations Using Naturalistic Driving Study Data

The objective of this paper is to analyze headway and speed distribution based on driver characteristics and work zone (WZ) configurations by utilizing Naturalistic Driving Study (NDS) data. The NDS database provides a unique opportunity to study car-following behaviors for different driver types in various WZ configurations, which cannot be achieved from traditional field data collection. The complete NDS WZ trip data of 200 traversals and 103 individuals, including time-series data, forward-view videos, radar data, and driver characteristics, was collected at four WZ configurations, which encompasses nearly 1,100 vehicle miles traveled, 19 vehicle hours driven, and over 675,000 data points at 0.1 s intervals. First, the time headway selections were analyzed with driver characteristics such as the driver’s gender, age group, and risk perceptions to develop the headway selection table. Further, the speed profiles for different WZ configurations were established to explore the speed distribution and speed change. The best-fitted curves of time headway and speed distributions were estimated by the generalized additive model (GAM). The change point detection method was used to identify where significant changes in mean and variance of speeds occur. The results concluded that NDS data can be used to improve car-following models at WZs that have been implemented in current WZ planning and simulation tools by considering different headway distributions based on driver characteristics and their speed profiles while traversing the entire WZ.

Examining the Feasibility of Using Naturalistic Driving Study Data for Validating Speed Prediction Models

Numerous curve speed models have been developed for purposes such as predicting driver behavior, evaluating roadway design consistency, and setting curve advisory speeds. With any model development efforts, questions can be raised about the transferability of the model between geographic regions. It is desirable to develop speed prediction models that can be used to predict vehicle speeds across different regions, as these models can then form the basis for consistent roadway analysis and evaluation methods. However, speed model validation and especially calibration are expensive tasks because of the need to collect field data. The main objective of this paper is to present a validation of speed prediction models for horizontal curves using the Naturalistic Driving Study (NDS) database. For this purpose, four two-lane rural highway sections from the State of Indiana were selected where each section includes multiple horizontal curves. Roadway design characteristics were used to predict the speed at the midpoint of each curve based on models that were previously calibrated using Texas data. Actual vehicle speeds at each curve were then computed and compared with the predicted speed using the Texas models. The results of the analysis suggest that the predicted speed from the Texas model provides an unbiased estimate of observed curve midpoint speeds in Indiana, and can provide good estimates of speeds at the beginning and ending of each curve with minor adjustment. The successful use of the NDS database for model validation shows that this database can facilitate similar efforts in additional states.

Complementary methodologies to identify weather conditions in naturalistic driving study trips: Lessons learned from the SHRP2 naturalistic driving study & roadway information database

Adverse weather conditions play a considerable role in the safety and efficiency of the transportation network. Many studies have aimed to quantify the impact that different weather conditions have on transportation safety and mobility; however, most studies have evaluated the network capacity, average speed, and other macroscopic measures without capturing specific driving characteristics. In order to understand specific driving behavior and performance characteristics that exist during different environmental conditions, high resolution vehicle data and video footage are required. The SHRP2 sponsored the generation of a large Naturalistic Driving Study (NDS) database – which provides vehicle time series data, front and rear video, driver video, external sensor readings, and driver surveys – and the Roadway Information Database (RID) – which is a complementary database with geospatial data for commonly driven roads in the NDS and other ancillary data sources, including annual traffic, roadway geometry, accident reports, weather conditions, and 511 alerts. The purpose of this study is to leverage these SHRP2 databases and weather data from the National Climatic Data Center (NCDC) to extract trips that occur during adverse weather conditions. The extraction of weather-related trips from a NDS is unprecedented, and this study presents three complementary methodologies used in parallel to acquire relevant trips from the SHRP2 NDS database. A semi-automated data reduction procedure was developed to process the raw trip files into a format that further analysis and modeling could be completed. This novel approach to NDS trip acquisition and reduction could be extended to other naturalistic driving studies worldwide.

The Influence of Different Factors on Right-Turn Distracted Driving Behavior at Intersections Using Naturalistic Driving Study Data

High frequency of distracted driving behavior is considered a high potential risk for traffic safety. Right-turn drivers’ distracted driving behavior can dramatically increase the crash risk considering the complex procedures required by the right-turn movements at intersections. This paper analyzed the influence of several factors including road geometry, environmental factors, and traffic conditions on the occurrence of right-turn drivers’ distracted driving activities. The data were collected through the Naturalistic Driving Study (NDS). A total of 581 events including 208 events with distracted driving and 373 events without distracted driving (baseline events) were extracted from the Strategic Highway Research Program 2 (SHRP 2) NDS database. The logistic model and random forest (RF) were applied for regression analysis. It was found that Vehicle Lane Occupied and Traffic Control are significantly correlated to distracted driving behavior in both models. The analysis of odds ratios indicated that dedicated right-turn lane design and adding yield sign at intersections can reduce the probability of having distracted driving behavior. Traffic density and driving time may also play important roles in the occurrence of distracted driving activities. Countermeasures are recommended to reduce distracted driving in this paper. The findings of this paper can help engineers and researchers better understand the dominant factors affecting drivers’ distraction. This research can also provide theoretical support for the distraction detection function in the advanced driver-assistance systems (ADAS).

Investigating the Impact of Fog on Freeway Speed Selection using the SHRP2 Naturalistic Driving Study Data

The negative effect of reduced visibility on driver performance has been recognized as one of the main causes of motor vehicle crashes in fog. Although many studies have concentrated on driver behavior during foggy weather in a simulated environment, there is a lack of studies that have addressed the impact of fog on driver behavior and performance in naturalistic settings. This paper utilized the data from the SHRP2 Naturalistic Driving Study (NDS) database to understand driver behavior in general and speed selection in particular during clear and foggy weather conditions. In this study, a comparative preliminary analysis and an ordered logit model were developed to evaluate driver speed behavior in fog and clear weather conditions. Results from the preliminary analysis showed 10% and 3% reduction in speed because of near fog and distant fog, respectively. In addition, results from the speed selection model showed that the odds of reducing speed were 1.31 and 1.28 times higher for drivers traveling in near fog and distant fog, respectively, compared with drivers who were driving in clear weather conditions. However, there is an over-representation of young drivers in the SHRP2 NDS database, which was reflected in the dataset used in this study. Therefore, a more representative sample of age groups might provide different results. The results from this study could provide a better insight into driver speed selection during foggy weather conditions, which can be utilized to improve various safety strategies including variable speed limits.

Distracted Driving and Risk of Crash or Near-Crash Involvement Among Older Drivers Using Naturalistic Driving Data With a Case-Crossover Study Design.

The purpose of this study was to examine the association between secondary task involvement and risk of crash and near-crash involvement among older drivers using naturalistic driving data. Data from drivers aged ≥70 years in the Strategic Highway Research Program (SHRP2) Naturalistic Driving Study database was utilized. The personal vehicle of study participants was equipped with four video cameras enabling recording of the driver and the road environment. Secondary task involvement during a crash or near-crash event was compared to periods of noncrash involvement in a case-crossover study design. Conditional logistic regression was used to generate odds ratios (ORs) and 95% confidence intervals (CI). Overall, engaging in any secondary task was not associated with crash (OR = 0.94, 95% CI 0.68-1.29) or near-crash (OR = 1.08, 95% CI 0.79-1.50) risk. The risk of a major crash event with cell phone use was 3.79 times higher than the risk with no cell phone use (95% CI 1.00-14.37). Other glances into the interior of the vehicle were associated with an increased risk of near-crash involvement (OR = 2.55, 95% CI 1.24-5.26). Other distractions external to the vehicle were associated with a decreased risk of crash involvement (OR = 0.53, 95% CI 0.30-0.94). Interacting with a passenger and talking/singing were not associated with crash or near-crash risk. Older drivers should avoid any cell phone use and minimize nondriving-related eye glances towards the interior of the vehicle while driving. Certain types of events external to the vehicle are associated with a reduced crash risk among older drivers.

Integrating Adaptive Lighting Database with SHRP 2 Naturalistic Driving Study Data

The limited information in traditional safety data makes exploration of the complex interaction between characteristics of roadway lighting and crash risks and driver behavior difficult, yet this knowledge is needed by transportation agencies that want to reduce energy consumption while maintaining safety. Data from the SHRP 2 Naturalistic Driving Study (NDS) have provided researchers a powerful tool for safety analysis. In addition, the availability of a massive adaptive lighting database (ALD) has made it possible to evaluate and improve the use of roadway lighting as a safety countermeasure. This paper integrates ALD and NDS data and provides insight into how other spatially related data can be integrated with the NDS data to cater to the needs of safety studies. The ALD includes detailed in situ roadway lighting measurements for more than 2,000 mi of major roads in seven states. This paper fills a gap in NDS-related documentation by detailing the NDS database structure and elements. An efficient spatial approach for integrating NDS data with ALD or other large roadway-related data sets in a similar format is presented. The approach uses a combination of existing and custom geographic information system tools, including a hybrid tool that uses attributes to perform the ArcGIS Near function.