Browsing by Author "Ridgeway, Christie"

Now showing 1 - 6 of 6
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    AI Dash Cam Performance Benchmark Testing
    Camden, Matthew C.; Soccolich, Susan A.; Ridgeway, Christie; Parks, R. Lucas; Hanowski, Richard J. (2023-06-30)
    The objective of this project was to benchmark the performance of three driver monitoring systems (DMSs): Motive DC-54, Samsara HW-CM32, and Lytx DriveCam SF400. The study was conducted in two phases. Phase One was an assessment to benchmark the performance of the three DMSs. This phase involved testing the ability of each system to successfully capture and alert unsafe driver behavior. Phase Two of the study, the user performance benchmarking phase, acquired feedback from heavy vehicle drivers regarding various attributes of each system’s quality. One hundred eighty-eight (188) CMV drivers with an active commercial driver’s license (CDL-A) participated in the survey.
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    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.
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    Challenges in Conducting Empirical Epidemiological Research with Truck and Bus Drivers in Diverse Settings in North America
    Soccolich, Susan A.; Ridgeway, Christie; Mabry, J. Erin; Camden, Matthew C.; Miller, Andrew M.; Iridiastadi, Hardianto; Hanowski, Richard J. (MDPI, 2022-09-30)
    Over 6.5 million commercial vehicle drivers were operating a large truck or bus in the United States in 2020. This career often has high stress and long working hours, with few opportunities for physical activity. Previous research has linked these factors to adverse health conditions. Adverse health conditions affect not only the professional drivers’ wellbeing but potentially also commercial motor vehicle (CMV) operators’ safe driving ability and public safety for others sharing the roadway. The prevalence of health conditions with high impact on roadway safety in North American CMV drivers necessitates empirical epidemiological research to better understand and improve driver health. The paper presents four challenges in conducting epidemiological research with truck and bus drivers in North America and potential resolutions identified in past and current research. These challenges include (1) the correlation between driving performance, driving experience, and driver demographic factors; (2) the impact of medical treatment status on the relationship between health conditions and driver risk; (3) capturing accurate data in self-report data collection methods; and (4) reaching the CMV population for research. These challenges are common and influential in epidemiological research of this population, as drivers face severe health issues, health-related federal regulations, and the impact of vehicle operation on the safety of themselves and others using the roadways.
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    Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles
    Yang, Guangwei; Ridgeway, Christie; Miller, Andrew M.; Sarkar, Abhijit (MDPI, 2024-04-12)
    Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.
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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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    Using Artificial Intelligence/Machine Learning Tools to Analyze Safety, Road Scene, Near-Misses and Crashes
    Yang, Gary; Sarkar, Abhijit; Ridgeway, Christie; Thapa, Surendrabikram; Jain, Sandesh; Miller, Andrew M. (National Surface Transportation Safety Center for Excellence, 2024-11-18)
    Artificial intelligence (AI) and machine learning technologies have the potential to enhance road safety by monitoring driver behavior and analyzing road scene and safety-critical events (SCEs). This study combined a detailed literature review on the application of AI to driver monitoring systems (DMS) and road scene perception, a market scan of commercially available AI tools for transportation safety, and an experiment to study the capability of large vision language models (LVLMs) to describe road scenes. Finally, the report provides recommendations, focusing on integrating advanced AI methods, data sharing, and collaboration between industry and academia. The report emphasizes the importance of ethical considerations and the potential of AI to significantly enhance road safety through innovative applications and continuous advancements. Future research directions include improving the robustness of AI models, addressing ethical and privacy concerns, and fostering industry-academic collaborations to advance AI applications in road safety.