cs.CVAug 24, 2026
SaveEG-ARSA: An Expert-Grounded Open Model for Visual Road Safety Auditing in Low-Resource Settings
Organizations: Department of Civil Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh
Abstract
Road traffic injuries remain a major challenge in low- and middle-income countries, where proactive road safety auditing is limited by incomplete crash records, shortages of qualified auditors, and the high cost of large-scale field inspections. To address this problem, we propose Expert-Grounded Distillation (EGD), a novel artificial intelligence framework that transfers institutional road safety expertise into a compact vision-language model for scalable visual road safety auditing. The key innovation is a quantified expert-grounding stage in which the teacher vision-language model is calibrated against authoritative field audits. Large-scale annotation is permitted only after the teacher reaches substantial agreement with expert risk assessments (Cohen's kappa = 0.74). The calibrated teacher then generates structured supervision that is distilled into an 8-billion-parameter student vision-language model using Low-Rank Adaptation and a single leakage-free prompt. We also introduce Bangladesh Road Safety Audit (BD-ARSA), the first open, expert-grounded Bangladeshi visual road safety audit dataset containing 21,947 image-audit records with near-national coverage, and Expert-Grounded Road Safety Auditor (EG-ARSA), the first vision-language model developed specifically for this task. Experimental results show that grounded fine-tuning substantially improves ordinal risk assessment over the zero-shot baseline, while blind expert evaluation demonstrates that the compact student outperforms both its 31 billion-parameter teacher and Gemini-2.5-Flash. These findings demonstrate that EGD provides an effective and scalable engineering solution for proactive road safety auditing in resource-constrained environments.
Figures & tables
| Category | Definition |
|---|---|
| vision_obstruction | Sight distance blocked by trees, walls, vegetation, shops, or structures at curves and junctions. |
| pedestrian_facilities | Footpath, crossing, or walkway absent or broken where pedestrian movement is high. |
| shoulder_condition | Paved or soft shoulder absent, narrow, or ineffective; no usable verge. |
| roadside_severity | Poles, trees, or solid structures within the clear zone ( 3 ft of the pavement edge). |
| road_markings | Centreline, edge line, lane marking, or crossing absent, faded, or poorly applied. |
| intersection | Junction geometry problems: staggered or skewed legs, poor flaring, uncontrolled crossings. |
Table 1: The 12 LGED visual road-safety hazard categories, with one-line definitions.
| Tier | Records | Label provenance |
|---|---|---|
| expert_gold | 343 | Single-hazard crops with auditor captions read directly from report 1 (72 audited locations); no teacher prompt. |
| expert_silver | 707 | Road-scene photos from report 2, image-aligned by us to the expert hazard findings (alignment manually verified) and curated into full structured audits under human review. |
| streetview | 20,897 | Sampled road-scene imagery; full teacher-VLM audits with no expert findings, validated against the 12-category taxonomy and risk-boundary post-processed. |
| Total | 21,947 |
Table 2: The three provenance tiers of BD-ARSA: record counts and label provenance.
| Train | Val | Test | Total | |
| By source tier | ||||
| expert_gold | 107 | 78 | 158 | 343 |
| expert_silver | 507 | 96 | 104 | 707 |
| streetview | 15,468 | 2,244 | 3,185 | 20,897 |
| All sources | 16,082 | 2,418 | 3,447 | 21,947 |
| By overall-risk level | ||||
Table 3: Location-disjoint splits by source tier and the overall-risk distribution per split.
Figure 1: Three-tier data provenance of BD-ARSA. Expert field audits from the ARI–BUET/LGED programme yield the gold (individually captioned hazard crops) and silver (road scenes we aligned to the expert findings and curated under human review) tiers; the street-view tier is teacher-audited road-scene imagery whose generation prompt was calibrated on the expert audits, with no per-image expert findings.
Figure 2: Nationwide street-view coverage. The 155 sampled road corridors span 63 districts across all 8 administrative divisions of Bangladesh. The expert-grounded gold and silver tiers are drawn from the 18 LGED-audited districts within this footprint.
Figure 3: A sample of BD-ARSA imagery, mixing gold single-hazard crops with street-view and silver road scenes across hazard categories and risk levels.
| Property | Value |
|---|---|
| Records (image–audit) | 21,947 (343 expert-gold / 707 expert-silver / 20,897 street-view) |
| Hazard instances (total) | 92,347 |
| Mean hazards per street-view scene | 4.22 (range 3–9) |
| Hazard categories | 12 (LGED schema) |
| Category entropy (normalised) | 0.79 (of 1.0) |
| Category Gini coefficient (concentration) | 0.53 |
Table 4: BD-ARSA corpus summary statistics (released dataset; metadata computed over all 21,947 records).
Figure 4: Distributional structure of BD-ARSA. (a) Hazard-category prevalence (log scale); (b) category composition within each provenance tier; (c) severity composition per category (Cramér’s ); (d) number of hazards per street-view scene (mean 4.2).
Figure 5: Hazard-category co-occurrence within records (Jaccard index). The four common categories form a tightly coupled cluster (up to 0.84); the rare categories are largely independent.
Figure 6: Hazard prevalence as a base-rate prior. (a) Lorenz curve of the 12-category instance distribution (Gini 0.53). (b) Per-category prevalence coloured by visual inferability; the two partly-non-visual categories ( drainage , skid_resistance ) — the model’s hardest set — sit at mid-range frequency, decoupling category difficulty from class scarcity.
Figure 7: The Expert-Grounded Distillation pipeline. Left: grounded data generation across the three tiers, curated into BD-ARSA (summarised here; full provenance in Fig. 1 ). Middle: the student architecture — a frozen vision encoder and projector feed image tokens, alongside text tokens, to the Qwen3-VL-8B-Instruct backbone, which is adapted with LoRA on the attention projections and trained with the multi-task objective and train-only logit adjustment. Right: single-image, leakage-free inference.
Figure 8: The student prompt (left) and the structured JSON audit target (right), with a worked street-view example. The prompt instructs the model to audit based only on what is visible in the image; the target lists hazards with an LGED category and severity, an ordinal overall risk, and a recommendation.
| Component | Setting |
|---|---|
| Base model | Qwen3-VL-8B-Instruct (vision encoder frozen) |
| Adaptation | LoRA, , , dropout 0.05, on |
| Precision | bf16 + gradient checkpointing |
| Image resolution | 1024 px (native max dimension) |
| Optimiser schedule | LR , cosine, 3% warmup |
| Effective batch | 16 (micro 2 accumulation 8) |
Table 5: Training configuration.
Figure 9: Training dynamics over the full run: per-task losses (hazard, risk, recommendation) and validation QWK against training steps, with the selected checkpoint marked. All task losses decrease and validation QWK rises across the two epochs.
| Model / setting | QWK | Linear | Macro-F1 | Accuracy |
|---|---|---|---|---|
| Zero-shot base (Qwen3-VL-8B) | 0.0772 [0.0440, 0.1082] | 0.0831 | 0.3585 | 0.5440 |
| Fine-tuned (raw) | 0.4815 [0.4538, 0.5097] | 0.4562 | 0.4933 | 0.7171 |
Table 6: Overall-risk prediction on the test set ( ): the fine-tuned student versus the zero-shot base. Brackets give bootstrap 95% confidence intervals.
| Risk class | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| Low | 0.667 | 0.020 | 0.038 | 102 |
| Medium | 0.632 | 0.714 | 0.671 | 1,381 |
| High | 0.787 | 0.756 | 0.771 | 1,964 |
Table 7: Per-class precision/recall/F1 for the fine-tuned student (raw operating point) on the test set.
Figure 10: Risk QWK and exact-risk accuracy for the zero-shot Qwen3-VL-8B-Instruct base versus the fine-tuned student, visualising the QWK gain from Expert-Grounded fine-tuning.
Figure 11: Overall-risk confusion matrices for the fine-tuned student: raw operating point (left) and -adjusted (right). Errors are predominantly between adjacent risk levels; the -adjusted matrix recovers Low recall at a small ordinal cost.
| Risk accuracy | Silver hazard R/P | |||
|---|---|---|---|---|
| Model | All | Gold | Silver | (recall / precision) |
| EG-ARSA (8B student) | 0.737 | 0.753 | 0.712 | 0.65 / 0.85 |
| Gemini-2.5-Flash | 0.592 | 0.544 | 0.663 | 0.79 / 0.77 |
| Teacher (31B, leakage-free) | 0.359 | 0.266 | 0.500 | 0.72 / 0.86 |
Table 8: Multi-model comparison on the 262 expert-grounded entries, under identical single-image, leakage-free prompts. Risk accuracy is reported overall and split by source; silver hazard-category recall/precision is also shown.
| Blind three-model comparison ( ) | ||
|---|---|---|
| Model | Overall (1–5) | Risk-correct |
| EG-ARSA (8B student) | 3.94 | 0.81 [0.73, 0.90] |
| Gemini-2.5-Flash | 3.65 | 0.58 |
| Teacher (31B, leakage-free) | 3.46 | 0.42 |
| EG-ARSA full expert-grounded evaluation ( ) | ||
| Issue correctness | 4.02 | |
Table 9: Blind human evaluation. Top: the 80-item blind three-model comparison (overall quality on a 1–5 scale and binary risk-correct). Middle: the student’s full expert-grounded evaluation ( ). Bottom: the cross-validation contrasting blind-human risk-correct with independent automated risk accuracy.
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
| Comparison | Value |
|---|---|
| Short labels — exact match (top-1 vs. top-1) | 40.4% |
| Short labels — top-2 inclusion | 60.6% |
| Short labels — Cohen’s kappa | 0.331 |
| Finding texts — mean Jaccard (multi-label) | 0.373 |
| Finding texts — perfect agreement | 12.3% |
| Finding texts — low agreement (Jaccard 0.5) | 61.5% |
Table 10: Inter-classifier agreement between the two zero-shot label generators (Gemini-2.5-Flash vs. BART-large-MNLI).
Figure 12: Word clouds of the expert corpus: (top) the detailed per-location findings layer, and (bottom) the independently-written auditor summary layer.
| Category | Findings-layer hits | Summary-layer hits |
|---|---|---|
| vision_obstruction | 1822 | 72 |
| roadside_severity | 1300 | 141 |
| intersection | 1212 | 176 |
| shoulder_condition | 1037 | 113 |
| embankment_safety | 915 | 154 |
| speed_management | 890 | 61 |
Table 11: Category keyword signal: keyword hits per hazard category in the detailed-findings layer and in the auditor-summary layer. Both independent layers cover all 12 categories.