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| Médium: | Recurso digital |
| Jazyk: | Mandarínština |
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Zenodo
2026
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| Témata: | |
| On-line přístup: | https://doi.org/10.5281/zenodo.20002992 |
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| _version_ | 1866901272542576640 |
|---|---|
| author | Yang, Wei |
| author_facet | Yang, Wei |
| contents | <p>Current autonomous driving multi‑sensor fusion systems generally adopt a post‑fusion (decision‑level) architecture. This paper identifies a fundamental defect of post‑fusion, precisely described as <strong>“1+1 < 1”</strong> – the overall performance after fusion is worse than that of the better single sensor. The paper proposes a complete end‑to‑end early‑fusion solution and distills four core principles: (1) <strong>1+1 < 1</strong> – the mathematical inevitability of performance degradation due to hard decision rules; (2) <strong>Allow deviation</strong> – abandon perfect spatiotemporal alignment, treat natural deviations as training features to enhance robustness; (3) <strong>Feature non‑conflict</strong> – convert multi‑sensor data into stacked feature channels processed by a single neural network, eliminating decision‑level conflicts; (4) <strong>Single‑model output</strong> – a single network directly outputs the final result, reducing latency and error accumulation. From information theory, probability, and engineering practice, the paper demonstrates these principles and provides a unified implementation framework. The proposed scheme theoretically surpasses the performance ceiling of post‑fusion architectures.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20002992 |
| institution | Zenodo |
| language | cmn |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | 多传感器端到端前融合——基于统一标注的特征级融合方案 Yang, Wei 多传感器融合;1+1<1;允许偏差;特征无冲突;端到端;前融合 <p>Current autonomous driving multi‑sensor fusion systems generally adopt a post‑fusion (decision‑level) architecture. This paper identifies a fundamental defect of post‑fusion, precisely described as <strong>“1+1 < 1”</strong> – the overall performance after fusion is worse than that of the better single sensor. The paper proposes a complete end‑to‑end early‑fusion solution and distills four core principles: (1) <strong>1+1 < 1</strong> – the mathematical inevitability of performance degradation due to hard decision rules; (2) <strong>Allow deviation</strong> – abandon perfect spatiotemporal alignment, treat natural deviations as training features to enhance robustness; (3) <strong>Feature non‑conflict</strong> – convert multi‑sensor data into stacked feature channels processed by a single neural network, eliminating decision‑level conflicts; (4) <strong>Single‑model output</strong> – a single network directly outputs the final result, reducing latency and error accumulation. From information theory, probability, and engineering practice, the paper demonstrates these principles and provides a unified implementation framework. The proposed scheme theoretically surpasses the performance ceiling of post‑fusion architectures.</p> |
| title | 多传感器端到端前融合——基于统一标注的特征级融合方案 |
| topic | 多传感器融合;1+1<1;允许偏差;特征无冲突;端到端;前融合 |
| url | https://doi.org/10.5281/zenodo.20002992 |