Seeing Risk Before It Happens: Risk-Augmented World Model for Anticipatory Driving Risk Understanding

Existing driving risk perception methods identify the safety-critical object only after the risk has become salient, leaving little time to react. We study anticipatory driving risk understanding, which localizes and describes a potential risk object as it will appear at a future risk moment, before the hazard becomes imminent. We address the task with a generate-then-perceive paradigm, in which a driving world model generates the future scene and risk understanding is performed within it. However, existing world models have no notion of safety criticality, making the generated risk object prone to distortion and blurring. We propose the Risk-Augmented World Model (RAWM), which augments the generation of the risk object with its appearance and semantic cues through a dual-branch conditioning module built on decoupled cross-attention. Moreover, a learnable risk prompt module estimates both cues from the early observation at inference without requiring risk annotations. On the DRAMA benchmark, RAWM consistently improves all localization and captioning metrics over the baseline world model and approaches the performance of an oracle model that observes the ground-truth future risk scene. Ablations on generation fidelity and downstream risk understanding attribute these gains to a more faithful generation of the future risk object. Compared to alternatives that either directly predict risks from the early observation or extrapolate risk-object trajectories, RAWM achieves increasingly larger gains as the anticipation horizon grows.