Publicação
Driver profile and drowsiness classification
| Resumo: | Abstract Every day, approximately 3,700 people die in road accidents, totaling 1.35 million fatalities globally each year. The primary causes of these accidents include speeding, distracted driving, drunk driving, nighttime driving, and drowsy driving. Changing human driving habits is extremely challenging, as road safety campaigns alone have shown limited impact on reducing fatalities. However, while influencing behavior is challenging, technological advancements offer promising solutions to mitigate risks and enhance road safety. This thesis explores unsafe driving behaviors, with a particular focus on both dangerous and drowsy driving. Using data from in-car sensors and physiological signals, we investigate ethods to assess driver states and identify patterns associated with an increased risk of accidents. Specifically, we analyze driving behavior metrics, such as speeding, harsh braking, and sudden acceleration, to classify risky driving tendencies. Additionally, we leverage heart rate variability features extracted from electrocardiograms recorded via a sensor-equipped steering wheel to detect signs of driver drowsiness. The results indicate that both behavioral and physiological markers can serve as effective indicators of unsafe driving conditions. In particular, aggressive driving behaviors are strongly linked to accident risk, while prolonged driving and fatigue significantly impair driver performance. Moreover, individual differences in responses to sleep deprivation highlight the need for personalized assessment methods. These insights contribute to the development of intelligent monitoring systems capable of identifying and mitigating unsafe driving conditions in real time, ultimately enhancing road safety. |
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| Autores principais: | Valente, Duarte Faria da Mota Gonçalves |
| Assunto: | Driver profile classification Driving style Drowsiness classification Drowsy driving i-DREAMS Heart rate variability Classificação de perfis de condução Condução sonolenta Estilos de condução Variabilidade da frequência cardíaca IPL/IDI&CA2024/ML4EP_ISEL |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Politécnica de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Científico da Universidade Politécnica de Lisboa |
| Resumo: | Abstract Every day, approximately 3,700 people die in road accidents, totaling 1.35 million fatalities globally each year. The primary causes of these accidents include speeding, distracted driving, drunk driving, nighttime driving, and drowsy driving. Changing human driving habits is extremely challenging, as road safety campaigns alone have shown limited impact on reducing fatalities. However, while influencing behavior is challenging, technological advancements offer promising solutions to mitigate risks and enhance road safety. This thesis explores unsafe driving behaviors, with a particular focus on both dangerous and drowsy driving. Using data from in-car sensors and physiological signals, we investigate ethods to assess driver states and identify patterns associated with an increased risk of accidents. Specifically, we analyze driving behavior metrics, such as speeding, harsh braking, and sudden acceleration, to classify risky driving tendencies. Additionally, we leverage heart rate variability features extracted from electrocardiograms recorded via a sensor-equipped steering wheel to detect signs of driver drowsiness. The results indicate that both behavioral and physiological markers can serve as effective indicators of unsafe driving conditions. In particular, aggressive driving behaviors are strongly linked to accident risk, while prolonged driving and fatigue significantly impair driver performance. Moreover, individual differences in responses to sleep deprivation highlight the need for personalized assessment methods. These insights contribute to the development of intelligent monitoring systems capable of identifying and mitigating unsafe driving conditions in real time, ultimately enhancing road safety. |
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