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This is a Quantitative Study study.

October 10, 2025Open Access

AutoDrive-QA: A Multiple-Choice Benchmark for Vision-Language Evaluation in Urban Autonomous Driving

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AutoDrive-QA enables reproducible evaluation of vision-language models in complex urban environments, enhancing safety.
Fine-tuning LLaVA-1.5-7B led to a six percentage point improvement in accuracy, demonstrating effectiveness across tasks.
Experiments showed GPT-4V reached a highest accuracy of 69.8% in zero-shot evaluations, outperforming competitors.
Traditional metrics like BLEU and CIDEr fall short in distinguishing model performance, indicating a need for new standards.

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AutoDrive-QA introduces multiple-choice questions to evaluate vision-language models in urban driving, providing standardized assessments.

Khalili et al. (2025) studied this question.

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AutoDrive-QA introduces multiple-choice questions to evaluate vision-language models in urban driving, providing standardized assessments.