This dissertation is situated at the intersection of aviation safety, sensor technology, and computational modeling, increasing airport apron safety by developing and testing optical sensing methods for automated apron surveillance. Central to this research is the utilization of Light Detection and Ranging (LiDAR) technology combined with computer vision algorithms for automatic scene understanding, complemented by tracking, motion prediction, and accident prediction functionalities for dynamic entities. Serving as the impetus for this research, an in-depth empirical analysis of 1220 aviation ground accident reports from 2008 to 2017 exhibits that 76 % of these occurrences could have been visually observed. Notably, the data reveals that 44 % of events indicate human failure, resulting from deficiencies in situational awareness among the involved parties. These findings highlight the opportunity for increasing airport safety by integrating automated surveillance methodologies. However, the ambitious endeavor of transitioning airport surveillance tasks to an automated system presents three main challenges. First, algorithms for automatic scene understanding rely on training datasets with ground truth annotations, which refer to semantic information representing real-world conditions. Such datasets do not exist for airport apron environments. Creating a training dataset for such environments involves scanning and manually annotating every aircraft type, ground vehicle, or object from multiple perspectives in every conceivable pose, velocity, and weather condition at multiple airports. Second, developing accurate tracking algorithms for aircraft relying on LiDAR point clouds requires time-synchronized true states for validation, which are not available. Third, recognizing visual features for accident prediction requires corresponding sensor data, which cannot be acquired in sufficient quantities given aviation's high safety standards and security-related access limitations to airport airside.
Thus, this dissertation addresses these challenges by developing a simulation environment that provides training data and a testing framework to develop recognition models and tracking algorithms for real-world applications, utilizing Dresden International Airport as the test field. This simulation environment includes 3D models of the test field, kinematic descriptions of aircraft ground movements, and a sensor model replicating LiDAR sensor behavior under different weather conditions. The simulation environment obviates real-world data acquisition and manual annotation by generating synthetic LiDAR scans, automatically annotated using context knowledge inherent to the simulation framework. Consequently, it enables training recognition models on synthetic data applicable to real-world data. The simulation environment can be adapted to any airport by modifying the static background elements, thus addressing the first challenge. Sensor positioning within the simulation is fully customizable. The developed motion models are formulated in a general manner, ensuring their functionality across any movement network. For validation purposes, a real LiDAR dataset was collected at the test airport and manually annotated. Competing recognition models were trained: employing real-world training data and the other leveraging synthetic training data. These models were tested on a real test dataset not seen during the training. The results show that the synthetic data-trained model achieves recognition performance comparable to, or even superior to, the real-data-trained model. Specifically, it demonstrates improved recognition of aircraft and weather-induced noise within the real test dataset. This enhanced performance is attributed to an overrepresentation of aircraft and weather effects in the synthetic training data.
The semantic segmentation model assigns semantic labels to each point of the point cloud. Tracking algorithms leverage this information to estimate the pose of objects. These estimations are crucial for verifying compliance with operational rules and to predict aircraft movement. Object positioning and orientation data inherent to the simulation enables the development and evaluation of tracking algorithms, addressing the second challenge. This research introduces an adaptive point sampling method for aircraft tracking that considers the velocity and spatial relationships of the tracked object, enhancing localization accuracy compared to conventional naïve sampling strategies in a simulated test dataset.
Finally, addressing the third challenge, the empirical study of accidents and incidents informing the generation of accident scenarios within the simulation environment. A kinematic motion prediction model, coupled with a deep learning architecture, is instrumental in establishing classifiers that distinguish between normal conditions and accident patterns. Evaluations conducted on a simulated test dataset have demonstrated considerable promise for accident and incident prediction while maintaining a minimal rate of false positives. The classifier has delivered lead times of up to 12 s before the precipitating event, facilitating adequate warnings for emergency braking in 80 % of the ground collision cases and 97 % of the scenarios involving infringements of holding restrictions within a test dataset. This result demonstrates a transformative potential for real-world applications, setting a new benchmark for preemptive measures in airport ground surveillance.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:93673 |
| Date | 11 September 2024 |
| Creators | Braßel, Hannes |
| Contributors | Fricke, Hartmut, Alam, Sameer, Technische Universität Dresden, Nanyang Technological University Singapore |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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