{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/116073"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/116073","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Enhancing Road Safety through Machine Learning for Prediction of Unsafe Driving Behaviors","abstract":"Road accidents pose a significant threat, leading to fatalities and injuries with far-reaching consequences. This study addresses two crucial challenges in road safety: analyzing traffic intersections to enhance safety by predicting potentially risky situations, and monitoring driver activity to prevent distracted driving accidents. Focusing on Virginia's intersections, we thoroughly examine traffic participant interactions to identify and mitigate conflicts, employing graph-based modeling of traffic scenarios to evaluate contributing parameters. Additionally, we leverage graph neural networks to detect and track potential crash situations from intersection videos, offering practical recommendations to enhance intersection safety. To understand the causes of risky behavior, we specifically investigate accidents resulting from distracted driving, which has become more prevalent due to advanced driver assistance systems in semi-autonomous vehicles. For monitoring driver activity inside vehicles, we propose the use of Video Transformers on challenging secondary driver activity datasets, incorporating grayscale and low-quality data to overcome limitations in capturing overall image context. Finally, we validate our predictions by studying attention modules and introducing explainability into the computer vision model. 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To understand the causes of risky behavior, we specifically investigate accidents resulting from distracted driving, which has become more prevalent due to advanced driver assistance systems in semi-autonomous vehicles. For monitoring driver activity inside vehicles, we propose the use of Video Transformers on challenging secondary driver activity datasets, incorporating grayscale and low-quality data to overcome limitations in capturing overall image context. Finally, we validate our predictions by studying attention modules and introducing explainability into the computer vision model. 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This study addresses two crucial challenges in road safety: analyzing traffic intersections to enhance safety by predicting potentially risky situations, and monitoring driver activity to prevent distracted driving accidents. Focusing on Virginia's intersections, we thoroughly examine traffic participant interactions to identify and mitigate conflicts, employing graph-based modeling of traffic scenarios to evaluate contributing parameters. Additionally, we leverage graph neural networks to detect and track potential crash situations from intersection videos, offering practical recommendations to enhance intersection safety. To understand the causes of risky behavior, we specifically investigate accidents resulting from distracted driving, which has become more prevalent due to advanced driver assistance systems in semi-autonomous vehicles. For monitoring driver activity inside vehicles, we propose the use of Video Transformers on challenging secondary driver activity datasets, incorporating grayscale and low-quality data to overcome limitations in capturing overall image context. Finally, we validate our predictions by studying attention modules and introducing explainability into the computer vision model. This research contributes to improving road safety by providing comprehensive analysis and recommendations for intersection safety enhancement and prevention of distracted driving accidents."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Road accidents are a serious problem causing numerous deaths and injuries each year. By studying driver behavior, we can uncover common causes of accidents like distracted driving, impaired driving, speeding, and not following traffic rules. New vehicle technologies aim to assist drivers, raising concerns about driver attentiveness. 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It is crucial for car manufacturers to develop systems that can detect and prevent accidents, especially in semi-autonomous vehicles. This study focuses on intersections in Virginia and examines driver behavior within vehicles to identify and prevent dangerous situations. We create models of different traffic scenarios using graphs/networks and utilize machine learning to identify potential accidents. Our objective is to provide practical recommendations for improving intersection safety. Existing datasets and algorithms for recognizing driver activities often fail to capture common distractions like eating, drinking, and phone use. To address this, we introduce two challenging datasets specifically designed to capture distracted driving activities. 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