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Semantic Road Lane Segmentation And Recognition Of Pedestrian Behavior For Autonomous Driving Vehicles

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dc.contributor.advisor Prof. Dailey, Matthew N.
dc.contributor.author Ravulapati, Kavya Samanvitha
dc.contributor.other Dr. Ekpanyapong, Mongkol
dc.contributor.other Prof. Dung, Phan Minh
dc.date.accessioned 2018-12-04T07:37:47Z
dc.date.available 2018-12-04T07:37:47Z
dc.date.issued 2018-12
dc.identifier.other AIT
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/921
dc.description 48 p. en_US
dc.description.abstract The computer vision and autonomous control communities are working towards fully automating the driving process in vehicles with the help of various tools such as object detection and traffic sign board recognition. Such tools play a major role in ensuring safety and preventing accidents. When designed with vigilance, we may create an automated, driverless society. One of the main obstacles to building such a system is to develop a good contextual model that can correctly understand what is going around the vehicle while it is driving. This may include detecting drivable areas and pedestrians. Even more challenging is to precisely interpret pedestrian actions and their latent meanings. Some possible pedestrian actions include running, waving hands, and looking. These are challenging actions for machine learning models to correctly interpret. The main reason behind this is that though their actions may be semantically the same, each pedestrian has his or her own way of performing the action. For example, the meaning of the action of waving hands may be to ask the driver to stop or to continue moving. Though such a task is instinctive to humans, it can be very ambiguous for the model to differentiate. The model should thus be trained well in order to interpret actions. Developing a system that accurately segments roads, detects pedestrians, and classifies their direction of motion in video streams during driving is the main idea of my research study. The system semantically segments the road lanes, detects bounding boxes for pedestrians on the road, and classifies the direction of motion of pedestrians into moving towards, away, left or right from the vehicle. Based on the decision taken after the classification of pedestrian behaviour, the system sends an alert message whether to stop, or to proceed with caution. Though the system performs well in the laboratory testing, it may not be ready for deployment in actual autonomous vehicles. en_US
dc.description.sponsorship AIT Fellowship en_US
dc.language.iso en_US en_US
dc.publisher AIT en_US
dc.subject Image segmentation en_US
dc.subject Semantic segmentation en_US
dc.subject Autonomous driving en_US
dc.subject Road lane segmentation en_US
dc.subject Pedestrian detection en_US
dc.subject YOLO object detection en_US
dc.subject Multinet architecture en_US
dc.subject Behavior classification en_US
dc.subject Tiny YOLO en_US
dc.subject Inception v3 en_US
dc.title Semantic Road Lane Segmentation And Recognition Of Pedestrian Behavior For Autonomous Driving Vehicles en_US
dc.type Research report en_US

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