Browsing by Author "Miller, Andrew M."

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    Behavior-Based Predictive Safety Analytics Phase II
    Miller, Andrew M.; Sarkar, Abhijit; McDonald, Tony; Ghanipoor-Machiani, Sahar; Jahangiri, Arash (Safe-D National UTC, 2023-06)
    This project addressed the emerging field of behavior-based predictive safety analytics, focusing on the prediction of road crash involvement based on individual driver behavior characteristics. This has a range of applications in the areas of fleet safety management and insurance, but may also be used to evaluate the potential safety benefits of an automated driving system. This project continued work from a pilot study that created a proof-of-concept demonstration on how crash involvement may be predicted on the basis of individual driver behavior utilizing naturalistic data from the Second Strategic Highway Research Program. The current project largely focuses on understanding and identifying the risks from a driver based on their driving behaviors, personal characteristics, and environmental influences. This project analyzed large scale continuous naturalistic data as well as event data to study the role of different driving behaviors in the buildup of risk related to a safety-critical event or crash. This research can be used structure the development of real-time crash risk that accounts for those identified driver behaviors to be evaluated across the contextualized information on a roadway.
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    Behavior-based Predictive Safety Analytics – Pilot Study
    Engström, Johan; Miller, Andrew M.; Huang, Wenyan; Soccolich, Susan A.; Machiani, Sahar Ghanipoor; Jahangiri, Arash; Dreger, Felix; de Winter, Joost (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-04)
    This report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical models predicting individual driver crash involvement based on individual driving style, demographic and behavioral history variables, using large sets of naturalistic driving data. The project was designed as a pilot project with the objective of providing the basis for a future more comprehensive research effort. Based on Second Strategic Highway Research Program (SHRP2) data, a subset of behavior and crash data including 2,458 drivers was created for analysis. The data were analyzed to investigate to what extent these drivers were differentially involved in crashes and near crashes, to what extent this was associated with individual characteristics, and if it is possible to predict individual drivers’ crash and near crash involvement based on variables representing individual characteristics. The results clearly demonstrated the presence of differential crash and near crash involvement and showed significant associations between enduring personal factors and crash involvement. Moreover, logistic regression and random forest classifiers were relatively successful in predicting crash and near crash involvement based on individual characteristics, but the ability to specifically predict involvement in crashes was more limited.
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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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    Evaluation of an In-vehicle Monitoring System Among an Oil and Gas Well Servicing Fleet
    Krum, Andrew; Miller, Andrew M.; Soccolich, Susan A. (National Surface Transportation Safety Center for Excellence, 2020-05-01)
    A pilot study of an in-vehicle monitoring system (IVMS) was conducted among a fleet of oil and gas well servicing vehicles. Data collected from the fleet were handled anonymously across 21 IVMS-instrumented light vehicle pickup trucks. Data were also collected on a sample of four participating drivers, one manager and three site workers, whose vehicles were instrumented with an IVMS and a miniature data acquisition system (MiniDAS). Among the 21 IVMS-instrumented trucks, there was a 60% reduction in speeding events and a 50% reduction in aggressive driving events. Questionnaires on the IVMS showed that drivers remained neutral to positive after the study was completed and rated the functionality of the IVMS positively. Analysis of the driving patterns of the four participants with MiniDAS-equipped vehicles showed long hours (average daily on-duty and commute time of 15.4 hours for the three site workers) and significant driving time on unimproved roads, which offer their own sets of hazards distinct from highway driving.
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    Trucking Fleet Concept of Operations for Automated Driving System-equipped Commercial Motor Vehicles
    Krum, Andrew; Mabry, J. Erin; Hanowski, Richard J.; Stojanovski, O.; Manke, A.; Adebisi, A.; Hammond, R.; Hickman, Jeffrey S.; Miller, Andrew M.; Camden, Matthew C.; Potts, I.; Harwood, D.; Jin, X.; Pugliese, B.; Ridgeway, C.; Werner, A.; Walker, M.; Kerns, L.; Meissner, K.; Parks, L.; Argueta, O.; Griffor, E.; Sarkar, A.; Stephens, M.; Yang, G.; Levin, J.; Faulkner, D.; Petersen, M.; Golusky, M.; Tidwell, S.; Crowder, T.; Bragg, H.; Terranova, P.; Thapa, S. (Federal Motor Carrier Safety Administration, 2024-07)
    The primary goals of the CONOPS project were to: i) collect information and practices on how to safely integrate ADS-equipped CMVs into the U.S. road transportation system; ii) provide the USDOT with data; iii) demonstrate how to integrate and deploy ADS-equipped trucks in a productive and cooperative way into the existing road freight ecosystem; and iv) collaborate with a broad and diverse group that includes government entities, university and research institutes, trucking associations, and private partners. This research found that the path forward to maintain public acceptance and achieve goals of ADS-equipped CMV operational cost-effectiveness, increased freight productivity, and reduction of crashes is through human operational assurance of vehicle, automation, freight, and public safety through specification, maintenance, inspections, monitoring, insurance, metrics, roadway assessment, and secure communications, as well as continuous lifecycle performance checks.
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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.
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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.