EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodo
Contemporary research in 3D object detection for autonomous driving primarily focuses on identifying standard entities like vehicles and pedestrians. However, the need for large, precisely labelled datasets limits the detection of specialized and less common objects, such as Emergency Medical Servic...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924011831 |
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| author | Chandra Jaiswal Sally Acquaah Christopher Nenebi Issa AlHmoud AKM Kamrul Islam Balakrishna Gokaraju |
| author_facet | Chandra Jaiswal Sally Acquaah Christopher Nenebi Issa AlHmoud AKM Kamrul Islam Balakrishna Gokaraju |
| author_sort | Chandra Jaiswal |
| collection | DOAJ |
| description | Contemporary research in 3D object detection for autonomous driving primarily focuses on identifying standard entities like vehicles and pedestrians. However, the need for large, precisely labelled datasets limits the detection of specialized and less common objects, such as Emergency Medical Service (EMS) and law enforcement vehicles. To address this, we leveraged the Car Learning to Act (CARLA) simulator to generate and fairly distribute rare EMS vehicles, automatically labelling these objects in 3D point cloud data. This enriched dataset, organized in the KITTI 3D object detection benchmark format by the Karlsruhe Institute of Technology and the Toyota Technological Institute, improves its utility for training and evaluating autonomous vehicle systems.To bridge the gap between simulated and real-world scenarios, our methodology integrates a wide range of scenarios simulation in CARLA, including variations in weather conditions, human presence, and different environmental settings. This approach enhances the realism and robustness of the dataset, making it more applicable to practical autonomous driving scenarios. The data provided in this article offers a valuable resource for researchers, industry professionals, and stakeholders interested in advancing autonomous vehicle technologies and improving emergency vehicle detection. Furthermore, this dataset contributes to broader efforts in road safety and the development of AI systems capable of handling specialized vehicle identification in real-world applications. |
| format | Article |
| id | doaj-art-67c1e97047d04bc9a7edc5ec01bcdd80 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-67c1e97047d04bc9a7edc5ec01bcdd802025-01-31T05:11:33ZengElsevierData in Brief2352-34092025-02-0158111221EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodoChandra Jaiswal0Sally Acquaah1Christopher Nenebi2Issa AlHmoud3AKM Kamrul Islam4Balakrishna Gokaraju5Corresponding author.; North Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United StatesNorth Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United StatesNorth Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United StatesNorth Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United StatesNorth Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United StatesNorth Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United StatesContemporary research in 3D object detection for autonomous driving primarily focuses on identifying standard entities like vehicles and pedestrians. However, the need for large, precisely labelled datasets limits the detection of specialized and less common objects, such as Emergency Medical Service (EMS) and law enforcement vehicles. To address this, we leveraged the Car Learning to Act (CARLA) simulator to generate and fairly distribute rare EMS vehicles, automatically labelling these objects in 3D point cloud data. This enriched dataset, organized in the KITTI 3D object detection benchmark format by the Karlsruhe Institute of Technology and the Toyota Technological Institute, improves its utility for training and evaluating autonomous vehicle systems.To bridge the gap between simulated and real-world scenarios, our methodology integrates a wide range of scenarios simulation in CARLA, including variations in weather conditions, human presence, and different environmental settings. This approach enhances the realism and robustness of the dataset, making it more applicable to practical autonomous driving scenarios. The data provided in this article offers a valuable resource for researchers, industry professionals, and stakeholders interested in advancing autonomous vehicle technologies and improving emergency vehicle detection. Furthermore, this dataset contributes to broader efforts in road safety and the development of AI systems capable of handling specialized vehicle identification in real-world applications.http://www.sciencedirect.com/science/article/pii/S2352340924011831CARLA simulatorAutonomous vehiclesPoint cloudMachine learningDeep learningTraffic scenarios |
| spellingShingle | Chandra Jaiswal Sally Acquaah Christopher Nenebi Issa AlHmoud AKM Kamrul Islam Balakrishna Gokaraju EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodo Data in Brief CARLA simulator Autonomous vehicles Point cloud Machine learning Deep learning Traffic scenarios |
| title | EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodo |
| title_full | EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodo |
| title_fullStr | EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodo |
| title_full_unstemmed | EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodo |
| title_short | EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodo |
| title_sort | ems3d kitti synthetic 3d dataset in kitti format with a fair distribution of emergency medical services vehicles for autodrive ai model trainingzenodo |
| topic | CARLA simulator Autonomous vehicles Point cloud Machine learning Deep learning Traffic scenarios |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924011831 |
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