Virtual Testing Environments for LiDARs: A Study of Scene Composition Effects
Bibliographic record
Abstract
Real-world testing, whether through naturalistic or test-track driving, cannot generate the test coverage required to prove the safety of autonomous vehicles with acceptable statistical significance. Virtual Testing Environments (VTEs) offer a complementary solution by enabling the creation of challenging safety-critical test scenarios that are difficult to replicate in real-world conditions. To ensure that a VTE is a sufficient representation of the real-world, our previous work proposed the development of a VTE using a digital twin of an actual roadway environment. This VTE can generate synthetic LiDAR scans, which can then be compared to real-world scans used to create the digital twin. We employed chamfer distance and density-aware chamfer distance as the metrics for this comparison. In this study, we implemented this approach on a variety of roadway scenes to investigate how different scene compositions affect the comparison metrics. Our findings reveal that factors such as road surface smoothness, vegetation presence, and the LiDAR’s point of view (elevation) significantly influence the comparison results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".