LingoQA: Visual Question Answering for Autonomous Driving

• URL: http://arxiv.org/abs/2312.14115v4
• Date: Thu, 26 Sep 2024 15:30:00 GMT
• Title: LingoQA: Visual Question Answering for Autonomous Driving
• Authors: Ana-Maria Marcu, Long Chen, Jan Hünermann, Alice Karnsund, Benoit Hanotte, Prajwal Chidananda, Saurabh Nair, Vijay Badrinarayanan, Alex Kendall, Jamie Shotton, Elahe Arani, Oleg Sinavski,
• Abstract summary: We introduce LingoQA, a novel dataset and benchmark for visual question answering in autonomous driving. The dataset contains 28K unique short video scenarios, and 419K annotations. On our benchmark, vision-language models respond truthfully to 59.6% of the questions compared to 96.6% for humans.
• Score: 14.620546951115328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce LingoQA, a novel dataset and benchmark for visual question answering in autonomous driving. The dataset contains 28K unique short video scenarios, and 419K annotations. Evaluating state-of-the-art vision-language models on our benchmark shows that their performance is below human capabilities, with GPT-4V responding truthfully to 59.6% of the questions compared to 96.6% for humans. For evaluation, we propose a truthfulness classifier, called Lingo-Judge, that achieves a 0.95 Spearman correlation coefficient to human evaluations, surpassing existing techniques like METEOR, BLEU, CIDEr, and GPT-4. We establish a baseline vision-language model and run extensive ablation studies to understand its performance. We release our dataset and benchmark as an evaluation platform for vision-language models in autonomous driving.

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