AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding
Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visual question answering. However, evaluating whether these models can reliably reason about safety-critical incidents remains challenging. To address this gap, we present AUTOPILOT-VQA, an incident-centric visual question answering benchmark for dashcam video understanding. The dataset evaluates different systems through structured questions designed around real-world driving inci
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- PossiblePossibly related (embedding) · 53%vlm-starter →
- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “AUTOPILOT VQA: Benchmarking Vision-Language Models for Incid” ≈ “VioletVision-3B””
- LinkedLinked via arxiv author · 85%Siddharth Damodharan →
“AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding”
- LinkedLinked via arxiv author · 85%Radhika Gupta →
“AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding”
- LinkedLinked via arxiv author · 85%Ali Alshami →
“AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding”
- LinkedLinked via arxiv author · 85%Ryan Rabinowitz →
“AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding”
- LinkedLinked via arxiv author · 85%Jugal Kalita →
“AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding”
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “AUTOPILOT VQA: Benchmarking Vision-Language Models for Incid” ≈ “pytorch/vision””