  Autonomous driving has long faced a challenge with public acceptance due to
the lack of explainability in the decision-making process. Video
question-answering (QA) in natural language provides the opportunity for
bridging this gap. Nonetheless, evaluating the performance of Video QA models
has proved particularly tough due to the absence of comprehensive benchmarks.
To fill this gap, we introduce LingoQA, a benchmark specifically for autonomous
driving Video QA. The LingoQA trainable metric demonstrates a 0.95 Spearman
correlation coefficient with human evaluations. We introduce a Video QA dataset
of central London consisting of 419k samples that we release with the paper. We
establish a baseline vision-language model and run extensive ablation studies
to understand its performance.
