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.