Abstract

Cooperative perception enables connected and autonomous vehicles to overcome the inherent limitations of individual sensors, such as occlusion and a restricted field of view, by sharing perceived objects among vehicles. While this enhances situational awareness, it introduces significant redundancy in dense traffic, leading to unnecessary computation and communication overhead. This paper proposes a novel redundancy-aware perception framework that integrates explicit object-level redundancy assessment directly into the detection stage. We extend the YOLOv11x architecture to ingest lightweight occupancy and inverse-distance masks derived from shared data, enabling the model to perform simultaneously 2D object detection and binary redundancy classification in a single inference step. On V2X-Sim, the proposed model achieves a redundancy F1-score of 0.972 with a measured forward-pass latency increase of 7.70 % (0.687 ms) relative to the RGB detector. By identifying redundant objects early, the framework enables resource-saving strategies, such as context-aware downsampling and selective offloading.

Keywords

MSC

68T45, 68T07, 93C85

Cite this article

@article{cai-9616,
  author  = {Nina Masarykova and Matej Halinkovic and Marek Galinski and Peter Truchly},
  title   = {Early Redundancy Assessment in Cooperative Perception Using a Modified YOLO Architecture},
  journal = {Computing and Informatics},
  volume  = {45},
  number  = {5},
  pages   = {1075--1099},
  year    = {2026},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2026_5_1075}
}
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