4D radar-camera fusion algorithm for intelligent navigation of inland unmanned vessels
Bibliographic record
Abstract
Inland unmanned vessels demonstrate significant application value in environmental monitoring, water rescue, and transportation. However, their perception systems face critical challenges including water surface reflections,adverse illumination, and variable weather conditions. Current aquatic perception research primarily relies on camera or low-resolution radar data, which fails to meet multimodal sensing requirements in complex scenarios. To address these challenges, this research proposes a 4D radar-camera fusion solution. We first construct a multidimensional feature representation system for 4D radar that dynamically extracts key features including distance, azimuth, velocity,and reflection intensity. Subsequently, we design a dynamic scene-adaptive cross-modal fusion mechanism that employs attention-based weighting to effectively integrate different modal features, enabling real-time adaptive algorithms to handle environmental variations. The heterogeneous sensors are then deeply coupled at the feature level through a carefully designed deep learning model. Experimental validation demonstrates significant improvements in environmental adaptability, with the proposed fusion solution achieving target detection accuracy improvements of3.4 and 3.9 percentage points over traditional vision systems under poor lighting and harsh weather conditions,respectively. This research not only provides an effective technical solution for intelligent perception in complex aquatic environments, but also advances the development of inland unmanned vessels toward intelligent and autonomous operation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".