ParCon: Noise-Robust Collaborative Perception via Multi-module Parallel Connection
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917801671786496 |
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| author | Bae, Hyunchul Kang, Minhee Ahn, Heejin |
| author_facet | Bae, Hyunchul Kang, Minhee Ahn, Heejin |
| contents | In this paper, we investigate improving the perception performance of autonomous vehicles through communication with other vehicles and road infrastructures. To this end, we introduce a novel collaborative perception architecture, called ParCon, which connects multiple modules in parallel, as opposed to the sequential connections used in most other collaborative perception methods. Through extensive experiments, we demonstrate that ParCon inherits the advantages of parallel connection. Specifically, ParCon is robust to noise, as the parallel architecture allows each module to manage noise independently and complement the limitations of other modules. As a result, ParCon achieves state-of-the-art accuracy, particularly in noisy environments, such as real-world datasets, increasing detection accuracy by 6.91%. Additionally, ParCon is computationally efficient, reducing floating-point operations (FLOPs) by 11.46%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ParCon: Noise-Robust Collaborative Perception via Multi-module Parallel Connection Bae, Hyunchul Kang, Minhee Ahn, Heejin Computer Vision and Pattern Recognition In this paper, we investigate improving the perception performance of autonomous vehicles through communication with other vehicles and road infrastructures. To this end, we introduce a novel collaborative perception architecture, called ParCon, which connects multiple modules in parallel, as opposed to the sequential connections used in most other collaborative perception methods. Through extensive experiments, we demonstrate that ParCon inherits the advantages of parallel connection. Specifically, ParCon is robust to noise, as the parallel architecture allows each module to manage noise independently and complement the limitations of other modules. As a result, ParCon achieves state-of-the-art accuracy, particularly in noisy environments, such as real-world datasets, increasing detection accuracy by 6.91%. Additionally, ParCon is computationally efficient, reducing floating-point operations (FLOPs) by 11.46%. |
| title | ParCon: Noise-Robust Collaborative Perception via Multi-module Parallel Connection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.11546 |