In real-world driving, resolving a primary hazard often precipitates a secondary one: swerving to avoid a stalled vehicle may force the ego car into oncoming traffic. Existing driving QA datasets fail to capture this cascading causality, evaluating perception, prediction, and planning in silos without anticipating the risks action induces. We introduce WaymoQA, a multi-view dataset that frames safety-critical reasoning as a two-stage task: resolving the immediate threat, then mitigating the action-induced secondary risk. WaymoQA provides 35,000 human-annotated QA pairs across image and video modalities, with 28K training and 6.4K test examples. Experiments show that SOTA models struggle on safety-critical scenes, while fine-tuning on WaymoQA improves both reasoning accuracy and consistency. WaymoQA thus serves as a rigorous benchmark and resource for safety-aware autonomous driving.

Long-form videos that span across wide temporal intervals are highly information redundant and contain multiple distinct events or entities that are often loosely related. Therefore, when performing long-form video question answering (LVQA), all information necessary to generate a correct response can often be contained within a small subset of frames. Recent literature leverage large language models (LLMs) in LVQA benchmarks, achieving exceptional performance, while relying on vision language models (VLMs) to convert all visual content within videos into natural language. Such VLMs often independently caption a large number of frames uniformly sampled from long videos, which is not efficient and can mostly be redundant. Motivated by this inefficiency, we propose LVNet, a modular and training-free framework featuring a novel Hierarchical Keyframe Selector (HKS) that efficiently selects a minimal set of informative frames tailored to each question. LVNet’s modularity allows easy integration with existing approaches for more efficient LVQA. We achieve state-of-the-art performance among similarly configured models across four benchmark LVQA datasets: EgoSchema, NExT-QA, IntentQA, VideoMME. The code can be found athttps://github.com/jongwoopark7978/LVNet