11 citations · 11 across the 4 of their papers we have counts for
4 papers
Uncertainty Estimation via Hyperspherical Confidence Mapping
Eunseo Choi, Ho-Yeon Kim, Jaewon Lee +3
Quantifying uncertainty in neural network predictions is essential for high-stakes domains such as autonomous driving, healthcare, and manufacturing. While existing approaches ofte…
DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Hyeongchan Ham, Heejin Ahn
Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on t…
Comparing Parameterizations and Objective Functions for Maximizing the Volume of Zonotopic Invariant Sets
Chenliang Zhou, Heejin Ahn, Ian M. Mitchell
In formal safety verification, many proposed algorithms use parametric set representations and convert the computation of the relevant sets into an optimization problem; consequent…
Recursively Feasible Chance-constrained Model Predictive Control under Gaussian Mixture Model Uncertainty
Kai Ren, Colin Chen, Hyeontae Sung +3
We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from…