2 citations · 2 across the 1 of their papers we have counts for
6 papers
Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures
Allen Wang, Xin Huang, Ashkan Jasour +1
This paper presents fast non-sampling based methods to assess the risk of trajectories for autonomous vehicles when probabilistic predictions of other agents' futures are generated…
Moment State Dynamical Systems for Nonlinear Chance-Constrained Motion Planning
Allen Wang, Ashkan Jasour, Brian Williams
Chance-constrained motion planning requires uncertainty in dynamics to be propagated into uncertainty in state. When nonlinear models are used, Gaussian assumptions on the state di…
Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles
Charles Dawson, Ashkan Jasour, Andreas Hofmann +1
Real-world environments are inherently uncertain, and to operate safely in these environments robots must be able to plan around this uncertainty. In the context of motion planning…
Non-Gaussian Chance-Constrained Trajectory Planning for Autonomous Vehicles under Agent Uncertainty
Allen Wang, Ashkan Jasour, Brian Williams
Agent behavior is arguably the greatest source of uncertainty in trajectory planning for autonomous vehicles. This problem has motivated significant amounts of work in the behavior…
Sequential Chance Optimization For Flow-Tube Based Control Of Probabilistic Nonlinear Systems
Ashkan Jasour, Brian Williams
In this paper, we address the problem of closed-loop control of nonlinear dynamical systems subjected to probabilistic uncertainties. More precisely, we design time-varying polynom…
Chance Constrained Motion Planning for High-Dimensional Robots
Siyu Dai, Shawn Schaffert, Ashkan Jasour +2
This paper introduces Probabilistic Chekov (p-Chekov), a chance-constrained motion planning system that can be applied to high degree-of-freedom (DOF) robots under motion uncertain…