A Comparison of Point Cloud Segmentation Models for Road Unevenness Detection and Smooth Autonomous Driving
Bibliographic record
Abstract
Detecting uneven and imperfect road segments is crucial for self-driving vehicles to ensure a safe and comfortable autonomous driving experience. This paper aims to explore the PointNet architecture across its many evolutions and examine how each generation performs when applied to road irregularity point cloud segmentation. The architectural differences between PointNet, PointNet++, and PointNeXt are explored, highlighting how each improves upon its predecessor. Customized versions of PointNet and PointNeXt are then developed and discussed, each obtaining overall training accuracies of 91% and 99.1%, respectively. These tailored implementations of PointNet and PointNeXt are then compared with an existing PointNet++ model for road unevenness segmentation, with our custom PointNeXt model demonstrating a dramatic 76% decrease in model size and a 0.7% jump in accuracy. Our findings highlight the significant improvements across PointNet generations, especially when used for LiDAR point cloud segmentation tasks. Moreover, this study demonstrates the superiority of PointNeXt for real-time road unevenness detection, opening avenues for further research into better autonomous driving solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".