Simultaneous Registration and Fusion of Multiple Dissimilar Sensors for Cooperative Driving
Bibliographic record
Abstract
The fusion of multiple sensory information plays a key role in cooperative driving for flexible platooning of automated vehicles over a couple of lanes within a short intervehicle distance. In this paper, the problem of online sensor fusion with spatially and temporally misaligned dissimilar sensors is considered. A spatial-temporal registration model for the popular intelligent vehicular sensors including radar, global positioning system, inertial navigation system, and camera is first developed for sensor alignment. An unscented Kalman filter (UKF) is proposed here to fuse and register these sensors that are installed on a platoon of vehicles simultaneously. When the road geometry information is available from a digital map database, a constrained UKF is further developed to improve the fusion accuracy. The effect of spatial-temporal sensor misalignment upon the vehicle-state estimation is also analyzed theoretically. Simulations show that the proposed UKF method not only can align the dissimilar vehicular sensors properly with both spatial and temporal biases, but can also obtain accurate fused tracks of vehicles in a platoon.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".