[
    {
        "id": "osp-16129",
        "type": "article-journal",
        "title": "How corner is a corner case? Percentile control for highway scenario generation",
        "author": [
            {
                "family": "Liu",
                "given": "Jiaxi"
            },
            {
                "family": "Zhou",
                "given": "Hang"
            },
            {
                "family": "Li",
                "given": "Hangyu"
            },
            {
                "family": "Wang",
                "given": "Yifan"
            },
            {
                "family": "Long",
                "given": "Keke"
            },
            {
                "family": "Ma",
                "given": "Chengyuan"
            },
            {
                "family": "Ran",
                "given": "Bin"
            },
            {
                "family": "Li",
                "given": "Xiaopeng"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/how-corner-is-a-corner-case-percentile-control-for-highway-scenario-generation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment. Existing autonomous-driving scenario generators can enforce specific behavior, adversity, or feasibility conditions, but they provide limited control over how extreme a generated scenario is relative to plausible futures in the same traffic context. This study represents the adversity of a generated scenario as its percentile in the conditional distribution of future risk given the observed history. This view supports calibrated answers to two questions: how \"corner\" a generated corner-case scenario is and how its \"cornerness\" can be fine-tuned. To this end, we formulate history-conditioned risk-percentile requests and learn a reference risk distribution that maps each requested percentile to a physical risk target. We then use a percentile-conditioned joint diffusion model with sampling-time risk guidance to generate multi-agent futures, together with a reference-based criterion for evaluating percentile realization. Experiments use the minimum post-encroachment time (PET) between the ego and its surrounding vehicles as the risk surrogate on highD. On the primary evaluation set, our method realizes 1,422 of 1,440 requests within a 0.05 percentile tolerance (98.75%), with mean percentile error 0.00673 and PET-target error 0.00991 seconds. The resulting interface connects context-relative risk specification, physical realization, and evaluation through a common risk scale. Project website and videos of generated scenarios are available at https://hhj233.github.io/CornerPercentile/."
    }
]