2 papers
eess.SY2026
Probabilistic Recursively Feasible Motion Planning Under Uncertain Environments
Hyeontae Sung, Hyeongchan Ham, Junyoung Park +2
Safe motion planning in uncertain, time-varying environments is challenging because the safe region can change unpredictably across planning steps, often causing a loss of recursiv…
cs.RO2025
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…