A Surrogate Metric-Based Framework for Placing Infrastructure Sensing Units to Enhance Cooperative Vehicle-Infrastructure Perception
Bibliographic record
Abstract
It is anticipated that roadside infrastructure sensing units (ISU) can cooperatively work with intelligent and connected vehicles (ICVs) to perceive traffic scenes more accurately when ICVs increasingly penetrate the market. However, the dynamic occlusion issue may still impair the cooperative vehicle-infrastructure perception capability (CVIPC). This study addresses the lack of an effective method for placing ISUs to augment CVIPC in a partially connected traffic environment. This study introduces probabilistic occupancy grids (POGs) to model the uncertainty of dynamic occlusions. The ground truth POG is estimated with a co-simulation method, while the observed POG by ISUs and ICVs are estimated using the proposed occlusion-considered ray-tracing algorithm. The cross entropy (CE) is applied to measure the difference between the ground truth and observed POGs and is used as a surrogate metric for estimating CVIPC. Setting ISUs’ placement parameters and POG-based CE as decision variables and the objective, respectively, Bayesian optimization (BO) is integrated with the multi-agent deep reinforcement learning (DRL) to maximize CVIPC. The test results imply that combining BO and DRL can outperform BO in optimizing ISUs’ placement. Compared to the simulation-in-the-loop optimization, the surrogate metric -based framework can achieve faster optimization with a small compromise on the optimized CVIPC measured by intersection over union. Traffic volume, traffic composition and ICV penetration rate all substantially affect CVIPC. In the test cases, as the ICV penetration rate reaches 50%, the observed POG is very close to the ground truth POG, and a further increase in the number of ICVs does not substantially contribute to improving CVIPC.
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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".