Towards Knowledge-based Road Modeling for Automated Vehicles: Analysis and Concept for Incorporating Prior Knowledge

Fricke, Jenny; Plachetka, Christopher; Rech, Bernd

doi:10.1109/ivworkshops54471.2021.9669220

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) 2021

DOI: 10.1109/ivworkshops54471.2021.9669220

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Towards Knowledge-based Road Modeling for Automated Vehicles: Analysis and Concept for Incorporating Prior Knowledge

Jenny Fricke

Christopher Plachetka

Bernd Rech

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2024

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Driving Through Graphs: a Bipartite Graph for Traffic Scene Analysis

Humnabadkar,

Sikdar,

Zhang

et al. 2024

2024 IEEE International Conference on Image Processing (ICIP)

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We introduce a novel approach for traffic scene analysis in driving videos by exploring spatio-temporal relationships captured by a temporal frame-to-frame (f2f) bipartite graph, eliminating the need for complex image-level highdimensional feature extraction. Instead, we rely on object detectors that provide bounding box information. The proposed graph approach efficiently connects objects across frames where nodes represent essential object attributes, and edges signify interactions based on simple spatial metrics such as distance and angles between objects. A key innovation is the integration of dynamic edge attributes, computed using Multilayer Perceptrons (MLP) by exploring this spatial metric. These attributes enhance our Interaction-aware Graph Neural Networks (IA-GNNs) framework by adapting the PageRankdriven approximate personalized propagation of neural predictions (APPNP) scheme and graph attention mechanism in a novel way. This has significantly improved our model's ability to understand spatio-temporal interactions of multiple objects in traffic scenarios. We have rigorously evaluated our approach on two benchmark datasets, METEOR and IN-TERACTION, demonstrating its accuracy in analyzing traffic scenarios. This streamlined, graph-based strategy marks a significant shift towards more efficient and insightful traffic scene analysis using video data.

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Driving Through Graphs: a Bipartite Graph for Traffic Scene Analysis

Humnabadkar,

Sikdar,

Zhang

et al. 2024

2024 IEEE International Conference on Image Processing (ICIP)

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show abstract

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Towards Knowledge-based Road Modeling for Automated Vehicles: Analysis and Concept for Incorporating Prior Knowledge

Cited by 1 publication

References 11 publications

Driving Through Graphs: a Bipartite Graph for Traffic Scene Analysis

Driving Through Graphs: a Bipartite Graph for Traffic Scene Analysis

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