National Surface Transportation Safety Center for Excellence Reports (NSTSCE, VTTI)

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https://hdl.handle.net/10919/5529

http://www.vtti.vt.edu/national/nstsce/

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100-car reanalysis: summary of primary and secondary driver characteristics
McClafferty, Julie A.; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2010-08-27)
This project's goal was to build a complete trip file inventory for the 100-Car data set... Data points collected for each file include Driver ID (with new IDs created as new secondary drivers were found), Ambient Lighting, Driver Seatbelt Usage, and an assessment of video operations/quality. --executive summary.
4U Lighting – Cooperative Headlighting
Palmer, Matthew; Tsuda, Hiroshi; Williams, Brian M.; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2019-10-31)
The purpose of this project was to evaluate the effectiveness of an alternative cooperative headlighting method, dubbed 4U Lighting. A human-subjects study was conducted in which 12 participants 65 or older observed pedestrians under different lighting configurations and identified the moment when they were sure they could see a pedestrian. The participants drove a vehicle towards a static vehicle in the opposite lane around which the pedestrians were located. The distance at which participants could detect the pedestrian, termed the detection distance, was compared across lighting conditions and served as the measure of improvement in driver visual performance (visibility). Commercial connected vehicle hardware and protocols were used to communicate position between the two vehicles and to trigger the operation of the custom lighting control system. The system operated as expected and the data showed benefits to driver visual performance.
Active and Adaptive Roadway Delineation Systems
Williams, Brian M.; Gibbons, Ronald B.; Flintsch, Alejandra Medina (National Surface Transportation Safety Center for Excellence, 2017-10-13)
Heavy fog presents a significant safety hazard to drivers by reducing their ability to see the roadway and other vehicles. Even so, drivers often fail to adjust their speed to account for the reduced visibility, resulting in stopping distances that exceed visibility distance. Active delineators, or in-pavement light emitting diode (LED) markers, are an emerging technology which can be used to assist drivers in traveling through fog-prone areas by marking road or lane boundaries. However, there is a lack of research indicating how the presence of active delineators might also affect a driver’s behavior. This study sought to examine how the presence of active delineators in fog might affect drivers’ speed. Three lighting patterns and two brightness levels were tested. In daytime conditions, the delineators had no effect on speed. During nighttime conditions, participants often drove faster when the active delineators were present. Participants felt that the active delineators were helpful for navigating through the fog in both daytime and nighttime conditions, but preferred the higher brightness settings in the daytime.
Aerial Traffic
Viray, Reginald; Saffy, Joshua; Mollenhauer, Michael A. (National Surface Transportation Safety Center for Excellence, 2024-08-30)
This report documents a significant advancement in work-zone safety through the strategic integration of aerial drone technology and machine vision software. It summarizes the project’s phases: Technical Assessment and Procurement, System Integration and Validation, and Deployment Assessment. The Technical Assessment and Procurement phase led to the selection of Smartek ITS’s DataFromSky product for its unique real-time processing capabilities of aerial drone video, making it superior to other commercial offerings. The System Integration and Validation phase ensured that the video streams, whether real-time or recorded, were processed effectively for varying roadway scenarios, including work zones and intersection monitoring. Accompanying development work included a user-defined data interface with the capability to trigger intruding vehicle alarms. The Deployment Assessment phase confirmed the system’s precision, with object detection up to 150 meters and sub-500 millisecond latency in relaying data for real-time alerts. Despite some GPS data discrepancies due to wind-induced drone movements, the system showed promise in controlled and real-world environments. Overall, the project acquired and validated the system’s functionality, with successful tests on live and recorded video feeds, software video processing, and real-time data transmission, culminating in the development of a robust intruding vehicle alarm mechanism. The system demonstrated great potential for deployment across various Virginia Tech Transportation Institute research initiatives, setting a precedent for future work in enhancing work zone safety.
Alcohol and Drug Testing: Informational Guidelines for Occupational Drivers
Glenn, T. Laurel; Camden, Matthew C.; Hickman, Jeffrey S. (National Surface Transportation Safety Center for Excellence, 2020-09-04)
The Omnibus Transportation Employee Testing Act of 1991 requires all Department of Transportation (DOT) agencies to conduct drug and alcohol testing of safety-sensitive transportation employees, which includes commercial motor vehicle (CMV) drivers. This report surveys the research literature to outline the effects that alcohol and drugs have on CMV driving safety and provides an overview of the most common testing methods. Additionally, the report provides guidelines on maintaining a drug-free workplace in the CMV industry, covering best practices for drug and alcohol policy, employee training, employee assistance programs, drug and alcohol testing, and drug and alcohol program evaluation.
Alcohol Intoxication Checklist: A Naturalistic Approach
Wotring, Brian; Antin, Jonathan F.; Smith, Ryan C. (National Surface Transportation Safety Center for Excellence, 2021-07-16)
This effort sought to determine the prevalence of particular visual and behavioral indicators for alcohol intoxication using data collected in the Strategic Highway Research Program 2 Naturalistic Driving Study (SHRP 2 NDS). A list of visual and behavioral cues was identified from previous research and served as the basis for identification. The prevalence of several of these cues reached statistical significance between judged states of intoxication. Some cues include, but are not limited to, lids-heavy, dozing, exhilarated, distracted, talkative, inability to sit upright, yawning, and leaning against window. While the study was able to determine the prevalence of the markers, several limitations temper interpretation. First, a large proportion of trips evaluated occurred between midnight and 4:00 a.m., when drivers are likely to be drowsy and exhibit many of the same visual and behavioral indicators also expected to be present in intoxicated individuals. Thus, impacts of drowsiness may be confounded with those of intoxication. In addition, the same visual cues were used both to determine the degree of intoxication as well as the behaviors most associated thereto, thus resulting in a logical conundrum. The results of this research should be viewed as exploratory work that can aid in the generation of hypotheses for future work.
Analysis of Car Cut-ins Between Trucks Based on Existing Naturalistic Driving Data
Sarkar, Abhijit; Engström, Johan; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2022-03-21)
For successful operation of cooperative adaptive cruise control, the participating vehicles follow certain operational criteria. For truck platooning, the participating trucks are required to maintain a minimum inter-vehicular distance for efficient vehicle-to-vehicle communication and better fuel efficiency. In order to ensure such operations, it is necessary to study the behavior of other agents in the roadway that may disrupt a platooning chain. A car cut-in is generally regarded as a disruption to the natural flow of traffic. In general, a cut-in takes place when a vehicle from the adjacent lane comes between the host vehicle and a lead vehicle, and in turn becomes the lead vehicle. In this work, we have searched 2.1 million miles of naturalistic truck driving data to identify candidate close cut-in scenarios. Then we analyzed approximately 18,500 cut-in cases to study the effects of car cut-ins under different platooning operating conditions, including following distance and headway. This study demonstrates the probability of cut-ins as a function of the following distance. The study found that the probability of cut-in increases when the host vehicle keeps a following distance greater than 23.5 meters. It also shows that as a result of a cut-in, the host vehicle often needs to brake and increase distance with the original lead vehicle by 15.5 meters. This scenario shows how the following vehicle reacts to a cut-in scenario. We further analyzed the safety implications of cut-in situations by computing the changes in time to collision. As the following behavior of the driver is one of the major factors governing cut-ins, we also analyzed the typical following behavior of the drivers in terms of following distance, following duration, following headway, and following speed. We assessed the safety of typical following distances through mathematical models. We believe that the study will significantly benefit researchers working with platoon control systems and coordinated platooning models to develop strategies towards the successful deployment of platooning and countermeasures for cut ins.
Analysis of Run-off-road Safety-critical Events in Virginia
Turturici, Marissa; Noble, Alexandria M.; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2021-05-27)
Run-off-road (ROR) crashes account for a large proportion of fatalities on U.S. roadways. ROR crashes usually involve a single vehicle and occur when the vehicle departs the roadway and then strikes an object. The research presented here analyzed data from three sources: police-reported data from ROR crashes involving teens in Virginia, and data from two naturalistic driving studies conducted with teens in Virginia (the Supervised Practice Driving Study and the Driver Coach Study). The data from the police reports were provided by the Virginia Department of Motor Vehicles (DMV) and Virginia Department of Transportation (VDOT). The two datasets were heterogeneous in terms of the method of data collection and types of ROR events that comprised the majority of cases (e.g., crashes vs. near-crashes). However, results showed several commonalities in ROR events involving teens and the characteristics of these events. For example, most ROR events occurred on dry roads for both datasets. In addition, ROR events were most common on straight roads with level alignment for both datasets. Finally, both datasets showed the highest proportion of ROR events in daylight, followed by darkness without lighting. Speeding was a common driver behavior noted in both datasets but was more common for the naturalistic dataset. Driver secondary tasks were difficult to compare across datasets because police reports often report no secondary task engagement or that it was not applicable to the case, whereas naturalistic driving data allows direct observation of secondary task engagement. Thus, in the DMV data, when secondary task engagement was observed, the most common task was using a cell phone, whereas the naturalistic data showed that talking with a passenger was most common.
Analyzing Intersection Gap Acceptance Behavior with Naturalistic Driving Data
Li, Yingfeng (Eric); Hao, Haiyan; Gibbons, Ronald B.; Medina, Alejandra (National Surface Transportation Safety Center for Excellence, 2022-09-14)
Safety at unsignalized intersections continues to be a major concern for transportation agencies and roadway users. To improve intersection safety, this project conducted a comprehensive study of gap acceptance behaviors at unsignalized intersections using the second Strategic Highway Research Program (SHRP 2) naturalistic driving study (NDS) data. The team collected 1,170 accepted and rejected gaps/lags based on 466 NDS trips at 60 unsignalized T-intersections in Washington state and North Carolina. The project team utilized a number of data sources, including time series data measuring vehicle kinematics for the analyzed trips, forward-facing and rear-view videos for the analyzed trips, driver demographic and driving history data, the SHRP 2 Roadway Information Database, and satellite images. First, the team identified the critical gaps for a number of common scenarios using three widely accepted methods: binary logistic regression, maximum likelihood method, and probability equilibrium method. Results showed an overall critical gap of 5.3 seconds for right-turning trips and 6.2 seconds for left-turning trips. The team then went on to develop a complete understanding of the factors affecting gap acceptance decisions using logistic regression and machine learning techniques. A number of factors were identified that affect drivers’ gap acceptance decisions, including being a gap instead of a lag, presence of leading and/or following vehicles, higher volume, intersection being unskewed, and increased number of through lanes. Finally, researchers further investigated drivers’ longitudinal and lateral acceleration behaviors during turning after accepting a gap and factors affecting their turning behaviors. Overall, both left- and right-turning vehicles initially accelerated quickly after they accepted a gap, and then reduced to a lower but prolonged acceleration rate while turning to reach a desired speed. For lateral acceleration, the peak value for the left-turning profile was reached later in the turning process than for the right-turning profile.
Application of Proximity Sensors to In-vehicle Data Acquisition Systems
Krothapalli, Ujwal; Stowe, Loren; Doerzaph, Zachary R.; Petersen, Andy (National Surface Transportation Safety Center for Excellence, 2018-05-02)
Naturalistic driving studies rely on human data reductionists to manually review and annotate driving behaviors. This work is time-consuming, and algorithms that could scan and categorize video data could make the data reduction process faster and more efficient. This report describes research to develop pose estimation methods that can be applied to drivers in naturalistic settings. Three methods were explored: (1) a depth-sensor-based pose estimation; (2) a deformable parts-based model; and (3) a tiny-image-based driver activity classifier. The tiny-image-based approach was chosen as the final solution and tested using the VTTIMLP01 dataset, a collection of about 80,000 images from 25 participants in naturalistic driving and simulated naturalistic driving conditions. The model was applied to approximately 50,000 images from the dataset covering seven activity classes: Eating/Drinking, Talking, Visor, Center Stack, Texting, One Hand on the Wheel, and Both Hands on the Wheel. The model, without any aspect ratio changes to the input image, was able to predict the activity classes with an overall 70% accuracy. To obtain better accuracies for individual activity classes, a separate model was built for each class, which resulted in a model with an overall accuracy of 74%. The Texting class had the poorest class accuracy (56%) due to the foreshortening effect on the limbs in the given camera angle. The One Hand on the Wheel class had the best class accuracy (96%).
Applying the Crash Trifecta Approach to SHRP 2 Data
Dunn, Naomi J.; Hickman, Jeffrey S.; Soccolich, Susan A.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2018-04-06)
The crash trifecta model does not consider crash genesis as a simple unitary element, but rather as a convergence of three separate, converging elements: (1) unsafe pre-incident behavior or maneuver; (2) transient driver inattention; and (3) an unexpected traffic event. Previous results from Phase I of the Crash Trifecta study showed that the presence of all three crash trifecta elements increased as the severity of a safety-critical event (SCE) increased. Given the limited number of crashes available in Phase I, however, it was not possible to identify trends in the presence of specific crash trifecta elements or to break the data down by incident type or crash severity. The current study built on the methods and results from Phase I by applying the crash trifecta model to the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS), which greatly increased the number of SCEs available for analysis. The results of Phase II show that elements well within a driver’s control are at the core of the majority of SCEs. Unsafe driving behavior was the most prevalent crash trifecta element, occurring in 70% of crashes and 52% of near-crashes. Unsafe driving behavior combined with transient inattention contributed to over 25% of crashes and almost 33% of at-fault crashes in the current study, compared to 5% of near-crashes and 8% of at-fault near-crashes, indicating that a crash is much more likely to occur if the unsafe driver is also not paying attention. The prevalence of the remaining two crash trifecta elements (i.e., transient inattention and unexpected event) varied depending on the severity of the SCE. An unexpected event was more likely to be present in near-crashes (74%) compared to crashes (25%), while the opposite was true for transient inattention near-crashes (28%) and crashes (43%). The increased number of SCEs in Phase II compared Phase I meant that the data set could be broken down by incident type for a more in-depth assessment of the applicability of the crash trifecta model. Of the 16 different incident types, the most common crashes were animal related, rear end (striking), rear end (struck), and road departure (left or right). The most common near-crashes were animal related, rear end (striking), sideswipe (same direction), and turn into path (same direction). The majority of different types of near-crashes tended to be associated with pedestrians, animals, pedalcyclists, or other vehicles behaving unexpectedly. The presence of transient inattention in a number of incident types resulted in a higher proportion of crashes than near-crashes. As was the case in Phase I, the results of the current Phase II study suggest that assigning a single, unitary critical reason as the proximal cause of the SCE without considering additional contributing factors is likely to be a limitation that does not address the complexities involved in the genesis of a crash.
Assessing Factors Leading to Commercial Driver Seat Belt Non-Compliance
Camden, Matthew C.; Soccolich, Susan A.; McSherry, Thomas; Ridgeway, Christie; Stapleton, Steven (National Surface Transportation Safety Center for Excellence, 2024-10-24)
The current research study utilized a literature review and analysis of two data sources to determine situational factors associated with reduced seat belt usage among CMV drivers. The literature review identified characteristics of seat belt use, reasons drivers may or may not use seat belts, methods to improve seat belt use rates, and important gaps in the literature. The data analysis used data collected in two separate studies to assess seat belt use rates and explore the relationship between seat belt use and environmental, roadway, vehicle, and driver factors. The first study collected observational data in 2015 from multiple sites in Michigan with high rates of truck/bus-involved crashes. The second study collected naturalistic driving data during the Federal Motor Carrier Safety Administration’s Advanced System Testing Utilizing a Data Acquisition System on Highways (FAST DASH) second Safety Technology Evaluation Project (commonly referred to as FAST DASH 2). The naturalistic driving data set included safety-critical events (SCEs), which were reduced for driver behaviors and environmental and roadway information. In the current study, driver seat belt use was observed in 93% of the FAST DASH 2 naturalistic driving SCEs and in 81% of SCEs in the observational data set. The analysis of observational and FAST DASH 2 naturalistic driving study data identified several factors where seat belt use patterns changed significantly across the factor levels; however, the analyses for each data set did not show consistency in statistical significance. The observational data showed seat belt use to be associated with day of week, time of day, road type, truck type, and fleet type. Little correlation was found between seat belt use and other driver behaviors. The analysis of observational study data did find seat belt use to be significantly higher in observations where drivers were using a hands-free cell phone with earpiece compared to drivers not using a cell phone or talking on a handheld cell phone. The naturalistic driving data showed that drivers operating on divided highways had higher seat belt use compared to those driving on non-physically divided roadways.
Assessing the Impact of Disability on Drivers’ Equitable Use of Advanced Driver Assistance Systems (ADAS): A Literature Review
Stulce, Kelly E.; Antin, Jonathan F. (NSTSCE, 2024-08-22)
The growing prevalence of advanced driver assistance systems (ADAS) in the U.S. passenger fleet promises increased mobility and enhanced safety outcomes for all drivers, but particularly for disabled drivers, a group that comprises 11.9% of the driving population (U.S. Bureau of Labor Statistics, 2021). For ADAS to realize their full potential, stakeholders need to consider the difficulties associated with ADAS use by disabled drivers as well as the potential benefits. To support this reckoning, the authors reviewed the extant literature to discover emerging themes and to identify gaps in the literature. We then synthesized these results into a proposed road map for future work that addresses the challenges of using ADAS to enhance mobility and improve safety for all drivers, including those who are disabled. Our review of the literature reveals gaps that point the way forward for further work that will support the optimal implementation of ADAS to compensate for disability-induced driving performance deficits. Specifically, our gap analysis and research road map suggest that this work should begin with using subjective methodologies (e.g., focus groups, interviews, and surveys) to learn from the disabled driver community in a manner that centers these individuals. Such research should yield results that more authentically capture the experience (or lack thereof) of disabled individuals driving with and making use of ADAS. Additionally, longitudinal research is necessary to support extended observation of real-world ADAS use by disabled drivers across driving environments and their disability-related functional states, which are often transient
Assessing the Safety Impact of Roadway Improvements Using Naturalistic Driving Data--Feasibility Study
Li, Yingfeng; Medina, Alejandra; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2017-10-19)
This project explored the feasibility of using Second Strategic Highway Research Program (SHRP 2) data, including the Roadway Information Database (RID), to evaluate the effectiveness of roadway safety improvements where traditional crash data are limited. The research team conducted two case studies based on naturalistic driving study (NDS) data from 200 trips. The two case studies evaluated the safety effects of (1) a paving project with newly installed pavement and markings, and (2) a median barrier replacement project with a newly installed and restored concrete median. A number of safety surrogate measures were used to develop a comprehensive understanding of how driver behavior changed with and without the safety treatment. The results from both case studies indicated that the roadway improvements had an impact on driver safety behavior, as indicated through the surrogate safety measures of speed, lateral and longitudinal accelerations, lane deviation, and car-following behavior. The two case studies illustrate two different methods for studying the effectiveness of roadway improvements on safety. The paving project case study compared driver behavior data collected at the project site after the roadway improvement with data from an adjacent site with similar roadway conditions but without the pavement improvement. The median barrier project case study compared data on the same segment of road before and after the improvement project. The two different methods illustrate the flexibility available with SHRP 2 safety data. In addition to the case studies, the research team also assessed the availability, suitability, and limitations of SHRP 2 and RID data for evaluating the safety impact of roadway improvements.
The Assessment of Alternative Overhead Sign Lighting
Williams, Brian M.; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2020-07-22)
This report evaluates an alternative method for lighting highway signs that takes advantage of their retroreflective properties. This method uses a single luminaire mounted some distance upstream of the sign, with a focused, but evenly distributed beam so that the sign always receives the same amount of illumination. To evaluate the proposed system, a human-subjects experiment was performed on the Virginia Smart Road to test two sign configurations: an overhead sign mounted on a gantry above the highway and a sign mounted at the side of the road on a horizontal curve. For the overhead sign, there were no statistical differences between the upstream lighting, traditional style lighting, and headlamp-only conditions, though there was an increase in the mean legibility distance of the sign (~14 m) when the upstream luminaire was located on the shoulder of the road at the highest luminance setting. For the horizontal curve, upstream lighting provided no benefit for sign legibility due to the ambient lighting in the vicinity, but placing the luminaire closer to the sign (approximately 20 m) resulted in more consistent luminance from a wider range of viewing angles. Although upstream lighting provided legibility distances similar to traditional sign lighting, it may offer advantages due to reduced maintenance and energy costs.
The Assessment of New Roadway Lighting in Rain and Fog
Williams, Brian; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2022-10-27)
This study sought to determine if different types of roadway lighting performed differently in rain or fog. The performance of the lighting was determined by participants’ ability to detect different objects along the shoulder of the road as they drove an experimental vehicle through simulated rain and fog at night on the Virginia Smart Roads Highway. Twenty-seven participants took part in this study, which consisted of three sessions: one for consenting and screening, one for performing the study in clear weather, and one for performing the study in rain and fog conditions.. As participants drove along the Smart Roads Highway, they looked for and verbally identified two types of objects that appeared on the right shoulder of the road. These included pedestrians wearing red, blue, or gray clothing, or small 7-inch square wooden targets painted red, blue, or gray. Participants also identified the color of the object. Participants performed these tasks under three different types of roadway lighting: traditional high-pressure sodium (HPS) lamps and two types of light-emitting diode (LED) lamps with different color temperatures (3500K and 6000K). Presentation orders of the lights and objects were counter-balanced to reduce learning effects. The performance of each light was determined by the distance at which participants could identify objects (detection distance) and the distance at which they could recognize the color of the objects (recognition distance). For each object and weather condition, a 2 (age) × 3 (lighting) × 3 (color) analysis of variance (ANOVA) was conducted with an alpha of 0.05. No significant difference was found among light types on participants’ ability to detect pedestrians in any weather condition. Results showed that all three light types performed equally for the detection of pedestrians in all weather conditions and for the detection of targets in clear and fog conditions. A significant difference was only found for the detection of targets in rain. However, there was no clear best performer as each light type performed well for the detection of some colors of targets and poorly for others. On average, detection distances for targets in the rain were approximately 10 m longer under the LED lights compared to the HPS.
An Assessment of Quiet Vehicles and Pedestrian and Bicyclist Safety
Alden, Andrew S. (National Surface Transportation Safety Center for Excellence, 2014-07-28)
The primary intent of this report is to provide a comprehensive and concise overview of the apparent safety issues presented to pedestrians and pedalcyclists by the operation of quiet vehicles on roadways. The report provides background information to establish how this issue became the focus of safety research in the United States and elsewhere. It presents the findings of a literature review of notable major research and a review of related pending and established regulations. The report also describes implemented and proposed countermeasure methods in addition to opportunities for future potential research to address knowledge gaps and improve overall understanding of the issues.
Assessment of the impact of color contrast in the detection and recognition of objects in a road environment: final report
Terry, Travis N.; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2011-12-16)
With the development of new light sources, options for the color and spectral output of a luminaire are wider than for traditional light sources. This can impact visibility as the spectral output of a light source may have a significant impact on the appearance of objects in the roadway environment. This study compares the visibility and color contrast afforded by three separate roadway luminaire types, each with a different spectral output. The benefit provided by the additional color information provided by the spectral distribution of a luminaire can improve detection of objects in the roadway by as much as 50%. These results, however, are not consistent across all spectral output and object color combinations. These results also indicate that the proper selection of a luminaire output will provide better visibility for the driver. -- Report website.
Bicycle Visibility: Conspicuity of Bicycle Headlamps, Tail Lamps, and Retroreflective Garments in Nighttime Roadway Environments
Bhagavathula, Rajaram; Gibbons, Ronald B.; Williams, Brian M.; Connell, Caroline A. (National Surface Transportation Safety Center for Excellence, 2020-07-21)
Cyclist deaths are overrepresented among traffic fatalities, and increasing cyclist conspicuity to drivers could potentially reduce cyclist deaths, particularly at night. This report describes an experiment with various commercially available bicycle visibility-enhancement systems in terms of their conspicuity to drivers during the day and at night. Visibility enhancements included a headlamp, tail lamp, spoke lights, and retroreflective clothing, including garments that highlight biomotion. The results indicate that active visibility treatments, such as bicycle-mounted lights, make cyclists more conspicuous than passive systems like retroreflective vests and biomotion bands. Flashing headlamps and tail lamps were the most conspicuous treatments during both the day and at night; fast flashing headlamps (6.7 Hz) had higher detection distances and rates during the day, and moderately fast flashing headlamps (3.4 Hz) had higher detection distances and rates at night. Spoke lights and flashing tail lamps, along with retroreflective vests, also aided cyclist visibility during the day and at night, especially for vehicles approaching intersecting cyclists. Passive retroreflective visibility treatments were most effective at night, when the vehicle was passing the cyclist from behind. However, that approach also used reflectors, so the discrete effect of passive retroreflective treatments could not be determined. This study also found that biomotion markers alone do not significantly increase cyclist conspicuity in visually complex natural environments. For most approaches, flashing lights had greater detection distances than biomotion markers, which in turn had higher detection rates than headlamps and tail lamps.
Camera-based Feature Identification for EasyMile Operation
Sarkar, Abhijit; Sundharam, Vaibhav; Manke, Aditi; Grove, Kevin (National Surface Transportation Safety Center for Excellence, 2022-11-15)
The EasyMile deployment studied in this work included cameras that captured the 360 degrees of roadway environment around the vehicle. We developed a scene perception algorithm using computer vision technology to track other roadway agents like cars, pedestrians, and bicyclists around the EasyMile LSAV. We used object detection and tracking algorithms to track the trajectories of each of the roadway agents. Then we used perspective geometry and camera specifications to find the relative distances and speeds of these agents with respect to the EasyMile. This helped us understand the configurations of the traffic around the LSAV and study other drivers’ temporal behavior. For example, the collected data shows the approach of any vehicle towards the EasyMile. Finally, we used this information to study other vehicles’ maneuvers and show how the information from the cameras can be used to study simple maneuvers of other vehicles such as cut-ins, lane changes, and following behavior. Through these camera-based tools, we have demonstrated examples from the real-world deployment. We studied following behavior characteristics that show the relative distance and speed of other vehicles’ following behavior. We have also demonstrated cut-in behaviors through the longitudinal and lateral trajectories of cut-in vehicles. We also showed how abrupt cut-ins may lead the EasyMile to apply its brakes, leading to safety critical events for following vehicles. Finally, we demonstrated how pedestrian behavior can be studied via these camera-based methods.

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