pDANSE enables nonlinear state estimation for model-free processes by using RNN-parameterized Gaussian priors and reparameterization-based particle sampling to compute posterior second-order statistics from nonlinear measurements.
Accurately computing the log-sum-exp and softmax functions
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A situation-aware hybrid feedback-predictive controller enables autonomous navigation in dense unstructured traffic by deriving reference speeds from braking distances and tracking virtual lanes from vehicle distributions.
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pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements
pDANSE enables nonlinear state estimation for model-free processes by using RNN-parameterized Gaussian priors and reparameterization-based particle sampling to compute posterior second-order statistics from nonlinear measurements.
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Situation-Aware Feedback-Predictive Control Framework for Lane-Less Dense Traffic
A situation-aware hybrid feedback-predictive controller enables autonomous navigation in dense unstructured traffic by deriving reference speeds from braking distances and tracking virtual lanes from vehicle distributions.