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03Applied AI Research·Research
VLM Transportation Research
A research project evaluating Vision-Language Models on Indian road-scene understanding across paired dashcam and top-view data.
role
Dataset engineering, annotation schema, evaluation
stack
Python · Qwen2.5-VL · Pandas · Google Colab · Custom Eval Harness
Overview
A research effort probing how well modern Vision-Language Models understand Indian road scenes when given paired viewpoints, structured prompts, and consistent evaluation.
Problem
VLMs perform impressively on generic benchmarks, but their behavior on unstructured, non-Western road scenes is under-explored. This project builds the dataset and harness needed to ask that question honestly.
Approach
- 01Pair dashcam and top-view footage; match frames to GPS timestamps.
- 02Design a compact annotation schema covering actors, hazards, and scene state.
- 03Prepare a curated evaluation subset with reviewer-verified labels.
- 04Run zero-shot VLM evaluation with a fixed prompt template.
- 05Compare multimodal inputs against single-view baselines with statistical and qualitative analysis.
My contributions
- Dataset engineering: collection, cleaning, frame-GPS matching.
- Annotation schema design and labeling guidelines.
- Evaluation pipeline and reporting.
- Qualitative failure-mode analysis across scene categories.
Outcomes
- → Reusable dataset structure for future VLM road-scene work.
- → A clear picture of where zero-shot VLMs help and where they break on Indian road data.
// scope · what this is not
- × No publication, acceptance, or peer review claimed.
- × Results are internal and exploratory.
// next.case_studies