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Using Visual Abstractions to Improve Spatially Aware Nominal Safety in Autonomous Vehicles

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dc.contributor.authorModak, Varun Nimishen
dc.contributor.committeechairZeng, Haiboen
dc.contributor.committeememberXuan, Jianhuaen
dc.contributor.committeememberWilliams, Ryan K.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2024-06-06T08:02:04Zen
dc.date.available2024-06-06T08:02:04Zen
dc.date.issued2024-06-05en
dc.description.abstractAs autonomous vehicles (AVs) evolve, ensuring their safety extends beyond traditional met- rics. While current nominal safety scores focus on the timeliness of AV responses like latency or instantaneous response time, this paper proposes expanding the concept to include spatial configurations formed by obstacles with respect to the ego-vehicle. By analyzing these spatial relationships, including proximity, density and arrangement, this research aims to demon- strate how these factors influence the safety force field around the AV. The goal is to show that beyond meeting Responsibility-Sensitive Safety (RSS) metrics, spatial configurations significantly impact the safety force field, particularly affecting path planning capability. High spatial occupancy of obstacle configurations can impede easy maneuverability, thus challenging safety-critical modules like path planning. This paper aims to capture this by proposing a safety score that leverages the ability of modern computer vision techniques, par- ticularly image segmentation models, to capture high and low levels of spatial and contextual information. By enhancing the scope of nominal safety to include such spatial analysis, this research aims to broaden the understanding of drivable space and enable AV designers to evaluate path planning algorithms based on spatial configuration centric safety levels.en
dc.description.abstractgeneralAs self-driving cars become more common, ensuring their safety is crucial. While current safety measures focus on how quickly these cars can react to dangers, this paper suggests that understanding the spatial relationships between the car and obstacles is just as important, and needs to be explored further. Prior metrics use velocity and acceleration of all the actors, to determine the safe-distance of obstacles from the vehicle, and determine how fast the car should react before a predicted collision. This paper aims to extend the scope of how safety is viewed during normal operating conditions of the vehicle by considering the arrangement of obstacles around it as an influencing factor to safety. By using advanced computer vision techniques, particularly models that can understand images in detail, this research proposes a new spatial safety metric. This score considers how well the car navigates through dense environments by understanding the spatial configurations that obstacles form. By studying these factors, I wish to introduce a metric that improves how self-driving cars are designed to navigate and path plan safely on the roads.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:40411en
dc.identifier.urihttps://hdl.handle.net/10919/119313en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAutonomous Vehiclesen
dc.subjectSpatial Awarenessen
dc.subjectNominal Safetyen
dc.subjectConvolutional Neural Networksen
dc.subjectSemantic Segmentationen
dc.titleUsing Visual Abstractions to Improve Spatially Aware Nominal Safety in Autonomous Vehiclesen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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