Network Level Clearance Assessment using LiDAR to Improve the Reliability and Efficiency of Issuing Over-Height Permits on Highways
Bibliographic record
Abstract
Commercial vehicles on highway networks are only permitted to use routes that are designed to accommodate their sizes and weights. When issuing an over-height permit, agencies consider vertical clearances on all route options on a highway network before directing a vehicle to the optimal route. Inefficient routing of commercial vehicles could cause excessive delays, which would result in undesirable economic impacts. Moreover, inaccurate assignment of vehicles to routes where the road infrastructure cannot handle the vehicle’s size could cause significant damage to the roadway, resulting in potential safety risks. Although routing programs and permit-issuing agencies try to avoid inaccurate assignments, this is not always possible since they rely on a database of information prone to human error and one which is not always up to date. To create a more reliable database of information, Departments of Transport need more efficient methods to collect information on highways. This paper aims to increase the efficiency of collecting data required to issue over-height permits by utilizing LiDAR data to automatically assess vertical clearance on highways. The method used involves detecting all overhead objects on a highway corridor and estimating the clearance at each object before mapping the data on a GIS map. The method was tested on three different highway corridors in Alberta, Canada ranging in length from 130 to 400 km. Testing revealed that the proposed method is effective in performing network-level assessment of vertical clearance, which has significant impacts on the efficiency of routing over-height vehicles on a network.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".