arXiv Machine Learning July 24, 2026 By Eni Solomon Laughter

SevDiff: Severity-Conditioned Diffusion for Long-Tail Conflict Trajectory Generation

Read the original on arXiv Machine Learning →

arXiv:2607. 20549v1 Announce Type: new Abstract: Trajectory datasets used in ADAS evaluation are heavily biased toward routine driving; genuine vehicle-to-vehicle conflict events are rare, and the rarer the event, the higher the cost when an ADAS system fails to handle it.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation

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