A binning-based Bayesian ROPE equivalence testing method is introduced to quantitatively assess practical equivalence between synthetic and real pre-crash scenario datasets for driving automation safety impact evaluation.
Advances in methods and practices in psychological science , volume=
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A statistical inference framework for day-to-day traffic dynamics models allows identifiability, consistency proofs, and parameter estimation from trajectory data, with extensions for heterogeneity and privacy, validated on simulations and Ann Arbor data.
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Practical validation of synthetic pre-crash scenarios
A binning-based Bayesian ROPE equivalence testing method is introduced to quantitatively assess practical equivalence between synthetic and real pre-crash scenario datasets for driving automation safety impact evaluation.
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Statistical Inference of Day-to-Day Traffic Dynamics
A statistical inference framework for day-to-day traffic dynamics models allows identifiability, consistency proofs, and parameter estimation from trajectory data, with extensions for heterogeneity and privacy, validated on simulations and Ann Arbor data.