Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario
This paper introduces the Scenario-Based Segmentation Network (SBS-Net), which highlights significant advances in autonomous driving. Through the integration of the Scenario Enhanced Graph Neural Network (SE-GNN) and graph re-match modules into the existing semantic segmentation network model based...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10400452/ |
| _version_ | 1827369219543531520 |
|---|---|
| author | Seungwoo Nham Jinho Lee Seongryul Yang Jihun Kim Shunsuke Kamijo |
| author_facet | Seungwoo Nham Jinho Lee Seongryul Yang Jihun Kim Shunsuke Kamijo |
| author_sort | Seungwoo Nham |
| collection | DOAJ |
| description | This paper introduces the Scenario-Based Segmentation Network (SBS-Net), which highlights significant advances in autonomous driving. Through the integration of the Scenario Enhanced Graph Neural Network (SE-GNN) and graph re-match modules into the existing semantic segmentation network model based on driver’s cognition (DCSeg-Net), our approach optimizes the graph construction process. The SE-GNN module enhances the extraction of critical features and relations within diverse driving scenarios, elevating both accuracy and the system’s adaptability to complex driving scenarios. The introduced graph re-match module refines classification discrepancies, significantly boosting segmentation accuracy and refining the understanding of autonomous driving scenes. Beyond technological enhancements, this work outlines the expansion and diversification of our original dataset, strengthening the learning capabilities of our model by including specific classification labels and incorporating a broader range of driving scenarios. The utility of the improved SBS-Net is demonstrated through superior performance in Graph Construction Accuracy and Intersection over Union (IoU) measures, as highlighted in our evaluation metrics. These advances underscore the practical applicability of scenario-based segmentation in real-world autonomous driving scenarios, enhancing overall scene comprehension capabilities. The developments presented signal substantial progress in the field of autonomous driving technology. |
| first_indexed | 2024-03-08T09:43:03Z |
| format | Article |
| id | doaj.art-bd418eed3e8641f881e4cac2d4ac2f84 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T09:43:03Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bd418eed3e8641f881e4cac2d4ac2f842024-01-30T00:01:45ZengIEEEIEEE Access2169-35362024-01-0112130881309910.1109/ACCESS.2024.335437910400452Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s ScenarioSeungwoo Nham0https://orcid.org/0000-0001-8196-5020Jinho Lee1https://orcid.org/0000-0001-6198-3168Seongryul Yang2https://orcid.org/0000-0002-0745-5636Jihun Kim3https://orcid.org/0009-0000-0607-5677Shunsuke Kamijo4Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Meguro City, Tokyo, JapanGraduate School of Interdisciplinary Information Studies, The University of Tokyo, Meguro City, Tokyo, JapanBeyless, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of KoreaBeyless, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of KoreaInterfaculty Initiative in Information Studies, The University of Tokyo, Meguro City, Tokyo, JapanThis paper introduces the Scenario-Based Segmentation Network (SBS-Net), which highlights significant advances in autonomous driving. Through the integration of the Scenario Enhanced Graph Neural Network (SE-GNN) and graph re-match modules into the existing semantic segmentation network model based on driver’s cognition (DCSeg-Net), our approach optimizes the graph construction process. The SE-GNN module enhances the extraction of critical features and relations within diverse driving scenarios, elevating both accuracy and the system’s adaptability to complex driving scenarios. The introduced graph re-match module refines classification discrepancies, significantly boosting segmentation accuracy and refining the understanding of autonomous driving scenes. Beyond technological enhancements, this work outlines the expansion and diversification of our original dataset, strengthening the learning capabilities of our model by including specific classification labels and incorporating a broader range of driving scenarios. The utility of the improved SBS-Net is demonstrated through superior performance in Graph Construction Accuracy and Intersection over Union (IoU) measures, as highlighted in our evaluation metrics. These advances underscore the practical applicability of scenario-based segmentation in real-world autonomous driving scenarios, enhancing overall scene comprehension capabilities. The developments presented signal substantial progress in the field of autonomous driving technology.https://ieeexplore.ieee.org/document/10400452/Driver’s scenariograph neural networkscene understandingsegmentation |
| spellingShingle | Seungwoo Nham Jinho Lee Seongryul Yang Jihun Kim Shunsuke Kamijo Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario IEEE Access Driver’s scenario graph neural network scene understanding segmentation |
| title | Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario |
| title_full | Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario |
| title_fullStr | Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario |
| title_full_unstemmed | Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario |
| title_short | Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario |
| title_sort | scenario based segmentation traffic image segmentation by gnn based driver x2019 s scenario |
| topic | Driver’s scenario graph neural network scene understanding segmentation |
| url | https://ieeexplore.ieee.org/document/10400452/ |
| work_keys_str_mv | AT seungwoonham scenariobasedsegmentationtrafficimagesegmentationbygnnbaseddriverx2019sscenario AT jinholee scenariobasedsegmentationtrafficimagesegmentationbygnnbaseddriverx2019sscenario AT seongryulyang scenariobasedsegmentationtrafficimagesegmentationbygnnbaseddriverx2019sscenario AT jihunkim scenariobasedsegmentationtrafficimagesegmentationbygnnbaseddriverx2019sscenario AT shunsukekamijo scenariobasedsegmentationtrafficimagesegmentationbygnnbaseddriverx2019sscenario |