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Browsing by Author "Manke, Aditi"

Now showing 1 - 5 of 5
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    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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    Fleet-based Driver Monitoring Systems: Accelerating Commercial Motor Vehicle and Occupational Driver Acceptance of Driver-facing Cameras
    Camden, Matthew C.; Glenn, T. Laurel; Manke, Aditi; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2022-10-14)
    Driver monitoring systems (DMSs) are an in-vehicle technology with promise to improve transportation safety for commercial motor vehicle (CMV) and occupational light vehicle drivers. DMSs include various sensors and cameras placed inside and outside a vehicle to record the surrounding environment and, in some cases, what the driver is doing behind the wheel. Many newer DMSs incorporate machine vision and artificial intelligence to detect environmental and behavioral factors in real-time, which may allow drivers to receive in-cab alerts associated with inattention and other driver errors found to be significant contributors to crashes. However, previous research has found that drivers are resistant to the use of driver-facing cameras. The purpose of this project was to discuss driver-facing cameras with CMV and occupational light vehicle drivers to identify their concerns and recommendations. Researchers conducted four focus groups with up to nine drivers per focus group. A total of 24 drivers participated across the four focus groups. The focus groups concentrated on three key concerns related to driver-facing cameras: driver privacy, micromanagement, and a lack of perceived safety improvements associated with driver-facing cameras. Although drivers often expressed resistance to driver-facing cameras, they did provide 12 recommendations that they believed would significantly reduce resistance: involve drivers early in the process of DMS implementation; establish a driver advisory group; use DMS data for performance recognition; use DMS data for safety competitions; be honest about the capabilities and functionality of driver-facing cameras; follow the data use policy; use a third party to review DMS data; ensure drivers understand what behaviors flag an event; ensure drivers know when an event was detected; give drivers leeway to correct behavior before management is notified; use data to show how DMSs improve safety; and limit audio recording.
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    Large Truck Safety at Highway Railroad Grade Crossings: Developing a Naturalistic Commercial Motor Vehicle Database of Railroad Grade Crossings
    Camden, Matthew C.; Manke, Aditi; Soccolich, Susan A.; Islam, Mouyid; Medina-Flintsch, Alejandra; Feierabend, Neal; Alemayehu, Desta (National Surface Transportation Safety Center for Excellence, 2022-10-12)
    In 2021, there were 2,131 collisions, 237 fatalities, and 653 injuries at highway-railroad grade crossings (RGCs). However, these data do not include the full scope of incidents at RGCs, as they do not account for vehicle-to-vehicle collisions (e.g., a rear-end collision when a lead vehicle stopped prior to traversing the RGC). This is especially true for the classes of commercial motor vehicles (CMVs) required to stop at all RGCs prior to crossing the tracks. The Virginia Tech Transportation Institute houses valuable data that may be used to better understand CMV driver behavior (and the behavior of other drivers near the CMV) at RGCs. One naturalistic driving study, the On-Board Monitoring System Field Operational Test (OBMS FOT), includes classes of CMVs required to stop at all RGCs: tanker trucks carrying oil/gas and motorcoaches. The objective of this project was to combine the OBMS FOT datasets with the Roadway Information Database and the Federal Railroad Administration’s Highway-Rail Crossing Inventory. Specifically, the purpose of this study was to identify all RGCs traversed by CMVs in the OBMS FOT (Hammond et al., 2021) datasets; identify which of those CMVs traversing an RGC were required to stop (i.e., a placarded CMV or motorcoach); identify the number of trips included in the OBMS that involved crossing an RGC; and create a database of RGCs that can be used in a future study to examine driver behavior of CMV and passenger vehicle drivers at RGCs. The final database included 1,733 RGCs traversed by a CMV that were in the OBMS FOT study. These vehicles made 52,358 trips across the 1,733 RGCs. This includes 17,990 trips of a tanker truck and 10,087 trips of a motorcoach traversing an RGC. This newly created database can be used in future research efforts to investigate CMV driver behavior at RGCs and to develop new countermeasures to reduce RGC crashes and their resulting injuries and fatalities.
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    Methods to Encourage Slow-moving Trucks to Travel in Designated Lanes
    Manke, Aditi; Ridgeway, Christie; Bell, Stephen "Roe" (National Surface Transportation Safety Center for Excellence, 2024-03-20)
    As the volume of traffic on highways increases, particularly heavy truck traffic, states throughout the United States are exploring innovative methods to enhance driver comfort, operational efficiency, and road safety. Instead of expanding roadways physically, more organizations are adopting a managed-lanes strategy. This approach assigns specific lanes with unique operational conditions to boost overall roadway performance in efficiency and safety. One popular application of this concept is lane restrictions for trucks. While drivers of smaller vehicles generally welcome these restrictions, research has shown mixed outcomes regarding safety and efficiency improvements. This project aimed to investigate new methods to improve the lane compliance of heavy vehicles, especially slow-moving trucks, on highways. Additionally, existing strategies for enforcement were explored, and new avenues were discussed for improving current restrictions. Six interviews with state Department of Transportation representatives, academic researchers, law enforcement officers, and truck drivers focused on three key areas: policy and enforcement, technological interventions, and effectiveness of interventions. In addition to the interviews, the Virginia 511 real-time traffic information system camera was observed to explore lane compliance violations in Virginia and the number of vehicles impeded due to the violations. Based on the results, 10 recommendations were identified to improve the operations and safety surrounding trucks, especially slow-moving trucks, on the highways.
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    Unravelling the Complexity of Irregular Shiftwork, Fatigue and Sleep Health for Commercial Drivers and the Associated Implications for Roadway Safety
    Mabry, J. Erin; Camden, Matthew C.; Miller, Andrew M.; Sarkar, Abhijit; Manke, Aditi; Ridgeway, Christiana; Iridiastadi, Hardianto; Crowder, Tarah; Islam, Mouyid; Soccolich, Susan A.; Hanowski, Richard J. (MDPI, 2022-11-10)
    Fatigue can be a significant problem for commercial motor vehicle (CMV) drivers. The lifestyle of a long-haul CMV driver may include long and irregular work hours, inconsistent sleep schedules, poor eating and exercise habits, and mental and physical stress, all contributors to fatigue. Shiftwork is associated with lacking, restricted, and poor-quality sleep and variations in circadian rhythms, all shown to negatively affect driving performance through impaired in judgment and coordination, longer reaction times, and cognitive impairment. Overweight and obesity may be as high as 90% in CMV drivers, and are associated with prevalent comorbidities, including obstructive sleep apnea, hypertension, and cardiovascular and metabolic disorders. As cognitive and motor processing declines with fatigue, driver performance decreases, and the risk of errors, near crashes, and crashes increases. Tools and assessments to determine and quantify the nature, severity, and impact of fatigue and sleep disorders across a variety of environments and populations have been developed and should be critically examined before being employed with CMV drivers. Strategies to mitigate fatigue in CMV operations include addressing the numerous personal, health, and work factors contributing to fatigue and sleepiness. Further research is needed across these areas to better understand implications for roadway safety.