COMAP: A SYNTHETIC DATASET FOR COLLECTIVE MULTI-AGENT PERCEPTION OF AUTONOMOUS DRIVING

12 citations as recorded by crossref.

1. GraphPS: Graph Pair Sequences-Based Noisy-Robust Multi-Hop Collaborative Perception C. Li et al. https://doi.org/10.1109/TIV.2023.3337656
2. SynthRoads: A framework for comprehensive enrichment of the Scan-to-Twin process for road space modeling with synthetic data D. Crampen et al. https://doi.org/10.1016/j.aei.2026.104410
3. Toward autonomous vehicles: A survey on cooperative vehicle-infrastructure system Y. Ji et al. https://doi.org/10.1016/j.isci.2024.109751
4. Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving Y. Yuan et al. https://doi.org/10.1109/LRA.2022.3143299
5. What can we learn from autonomous vehicle collision data on crash severity? A cost-sensitive CART approach S. Zhu & Q. Meng https://doi.org/10.1016/j.aap.2022.106769
6. V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving Y. Li et al. https://doi.org/10.1109/LRA.2022.3192802
7. Collaborative Perception Datasets for Autonomous Driving: A Review N. Wang et al. https://doi.org/10.1109/JSEN.2025.3582040
8. A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook M. Liu et al. https://doi.org/10.1109/TIV.2024.3394735
9. Cooperative Camera-LiDAR Extrinsic Calibration for Vehicle-Infrastructure Systems in Urban Intersections Y. Xiong et al. https://doi.org/10.1109/JIOT.2025.3567973
10. All you need for object detection: From pixels, points, and prompts to Next-Gen fusion and multimodal LLMs/VLMs in autonomous vehicles S. Boroujeni et al. https://doi.org/10.1016/j.imavis.2026.105944
11. StigDiff: Stigmergy Diffusion for Signal Compensation in Multi-Agent Localization Y. Zhang & R. Xie https://doi.org/10.1109/OJITS.2026.3694707
12. Cooperative Perception for Automated Driving: A Survey of Algorithms, Applications, and Future Directions C. Wei et al. https://doi.org/10.1109/JPROC.2025.3608874