Introduces a path-based credibility framework using equivalent rainfall intensity, RRD scores from real raindrop spectra, and lidar consistency metrics to identify preferable simulated rainfall paths for AV perception tests.
A survey on scenario-based testing for automated driving systems in high- fidelity simulation,
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The paper proposes a paradigm of provable probabilistic safety to enable scalable, safe deployment of embodied AI in critical applications.
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From Nominal Intensity to Equivalent Rainfall: A Path-Based Credibility Evaluation Framework for Simulated Rainfall in Autonomous-Driving Perception Tests
Introduces a path-based credibility framework using equivalent rainfall intensity, RRD scores from real raindrop spectra, and lidar consistency metrics to identify preferable simulated rainfall paths for AV perception tests.
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Towards provable probabilistic safety for scalable embodied AI systems
The paper proposes a paradigm of provable probabilistic safety to enable scalable, safe deployment of embodied AI in critical applications.