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arXiv AI By Manasa Mariam Mammen, Zafer Kayatas, Stefan Wagner

Evaluating Physical Consistency and Plausibility in Generative Scenario Models for Autonomous Driving

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The paper introduces a layered evaluation protocol for generative scenario models used in autonomous driving, focusing on physical consistency and plausibility. It examines internal representations through kinematic alignment, statistical baseline comparison, latent controllability, and activation analysis, and then tests outputs against vehicle dynamics constraints such as lateral jerk thresholds. The protocol is applied to a VAE-based scenario generator and other generative models, revealing deeper insights than standard output-level metrics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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