Edge Computing-Based Collaborative Vehicles 3D Mapping in Real Time
Why this work is in the frame
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Bibliographic record
Abstract
Cooperative vehicles are better able to detect the environment and self-localize than a single vehicle. Cooperative vehicles can quickly cover the entire environment by communicating and cooperating with each other and can also reduce localization and mapping error by merging the cooperative vehicle information from observation and navigation. In this paper, we propose a novel algorithm for an effective solution of navigation and mapping for cooperative vehicles in an unknown environment. We present an improved centralized and collaborative monocular simultaneous localization and mapping (CCM-SLAM) approach. The proposed algorithm can accurately compute the transformation matrix for cooperative vehicle maps and reduce the communication delay, data loss among vehicles and decrease the bandwidth demand. The quaternion and credibility similarity transformation (QC-Sim(3)) method we proposed is used to accurately merge the matched maps and accomplish loop closures. The sending messages at variable frequencies (SMVF) method we proposed and an improved detection and resending lost data (I-DRLD) method we proposed can improve the accuracy of pose estimation. SMVF solves the time-delay problem by sending messages to the vehicles at flexible frequencies while I-DRLD detects and resends the lost data. We also adopt Intra-frame Feature Compression (IFC) to decrease the bandwidth demand in the process of the transmitting data. The experiments demonstrate the superiority of our proposed algorithm compared with the state-of-the-art methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it