BEVSeg2GTA: Joint Vehicle Segmentation and Graph Neural Networks for Ego Vehicle Trajectory Prediction in Bird's-Eye-View

Predicting the trajectory of the ego vehicle is a critical task for autonomous vehicles. Even though traffic regulations have defined boundaries, various behaviors of the agents in real-life situations introduce complexities that are hard to capture comprehensively. This has led to a rising curiosit...

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Veröffentlicht in:	IEEE access Jg. 12; S. 132159 - 132174
Hauptverfasser:	Sharma, Sushil, Das, Arindam, Sistu, Ganesh, Halton, Mark, Eising, Ciaran
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Autonomous vehicles Bird’s-Eye-View Cameras encoder-decoder transformer Encoders-Decoders Encoding Feature extraction graph neural network Graph neural networks Image segmentation Multi-view camera Neural networks Pedestrians Perception Predictions Probabilistic logic Semantic segmentation Spatial temporal resolution spatio-temporal probabilistic network State-of-the-art reviews Task complexity Trajectory trajectory prediction Transformers Vehicles
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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