Drowsiness and Emotion Detection of Drivers for Improved Road Safety
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14732 LNCS, Page: 13-26
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Often resulting in fatalities and serious injuries, drowsy driving is a key contributing factor in collisions. Drivers who are irritable or disturbed are more likely to be involved in accidents. Identifying symptoms of exhaustion and negative emotions may facilitate taking preventative measures before an accident occurs. By identifying and responding to the driver’s state, cars equipped with sophisticated technology such as drowsiness and emotion detection aim to enhance the driving experience. With the use of machine learning algorithms, these cars can accurately read facial expressions to determine the driver’s drowsiness and emotion. The technology may alter the vehicle’s behaviour and user interface in response to the driver’s state, making driving safer and more personalised. To detect driver’s drowsiness different approaches are taken in this experiment such as the use of Basic CNN, ResNet50, VGG16, VGG19 and InceptionV3. Among them InceptionV3 has given 100% accuracy in the process of detecting drowsy drivers for yawn_eye_dataset_new. In case of NITYMED dataset where video data were used, Resnet50 produced 100% accuracy. We have used InceptionV3 for detecting driver’s emotion for FER2013 Dataset, which has given 68.02% accuracy. We conducted a survey on drivers regarding what affects them at the time of driving and whether a continuous monitoring system will help them or not. Almost everyone agreed on having a monitoring system for road safety.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85196758124&origin=inward; http://dx.doi.org/10.1007/978-3-031-60477-5_2; https://link.springer.com/10.1007/978-3-031-60477-5_2; https://dx.doi.org/10.1007/978-3-031-60477-5_2; https://link.springer.com/chapter/10.1007/978-3-031-60477-5_2
Springer Science and Business Media LLC
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