4 papers
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…
Knapsack-based Online Sensor Selection for Vehicle State Estimation
Jehyeop Han, Minhee Kang, Alessandro Colombo +2
As connected and autonomous driving technologies advance, vehicles increasingly rely on data from external sensors. Although this information can enhance state estimation, processi…
Sampling-Based Safety Filter with Probabilistic Restrictiveness Guarantee
Junyoung Park, Hyeontae Sung, Heejin Ahn
Ensuring safety is a critical requirement for autonomous systems, yet providing formal guarantees for nominal controllers remains a significant challenge. In this paper, we propose…
Chance-Constrained Trajectory Planning with Multimodal Environmental Uncertainty
Kai Ren, Heejin Ahn, Maryam Kamgarpour
We tackle safe trajectory planning under Gaussian mixture model (GMM) uncertainty. Specifically, we use a GMM to model the multimodal behaviors of obstacles' uncertain states. Then…