Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units
Bibliographic record
Abstract
Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real-life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes a virtual-real-fusion simulation (VRFS) framework that combines traffic simulation and point clouds of real-world road environment to optimize RSUs’ deployment. Point clouds and triangular meshes are used to model static and dynamic obstacles, respectively. A structure-retained spherical projection method is developed to efficiently emulate RSUs’ data collection. Based on the developed VRFS, the probabilistic occupancy maps (POM) are created to represent traffic scenarios. The POM-based cross entropy (CE) is proposed as the surrogate metric for evaluating the detection performance of cooperative RSUs. The Bayesian optimizer is applied to optimize the RSUs’ placement parameters (decision variables) by minimizing CE. Test results show that it is viable to use the POM-based CE as a proxy for evaluating cooperative RSUs’ sensing performance. Considering the occlusion effect adds to the efficacy of POM-based CE as a surrogate metric. Compared with traffic volume, the adverse effect of the proportion of large vehicles on RSUs’ detection performance is more significant. There are no significant patterns regarding how the optimized RSU positions vary with traffic parameters. The comparisons with existing methods further verify the importance of considering both static and dynamic occlusions in optimizing RSUs’ placement. Besides, the proposed method can yield better optimization results more efficiently than existing approaches.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".