1 code implementation • 26 May 2023 • Ashkan Jasour, Weiqiao Han, Brian Williams
To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem.
no code implementations • 2 Mar 2023 • Weiqiao Han, Ashkan Jasour, Brian Williams
We consider the motion planning problem for stochastic nonlinear systems in uncertain environments.
no code implementations • 2 Mar 2023 • Weiqiao Han, Ashkan Jasour, Brian Williams
In particular, in the provided optimization problem, we use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems.
no code implementations • 17 Oct 2021 • Xin Huang, Guy Rosman, Ashkan Jasour, Stephen G. McGill, John J. Leonard, Brian C. Williams
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples.
no code implementations • 5 Oct 2021 • Xin Huang, Guy Rosman, Igor Gilitschenski, Ashkan Jasour, Stephen G. McGill, John J. Leonard, Brian C. Williams
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction.
1 code implementation • 21 Sep 2021 • Ashkan Jasour, Xin Huang, Allen Wang, Brian C. Williams
The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models to predict both agent positions and control inputs conditioned on the scene contexts.
1 code implementation • 4 Aug 2021 • Xin Huang, Meng Feng, Ashkan Jasour, Guy Rosman, Brian Williams
In this paper, we propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic and produce risk-bounded policies efficiently by adding an extra risk term in the loss function of the policy network.
1 code implementation • 29 Jan 2021 • Ashkan Jasour, Allen Wang, Brian C. Williams
Moments of uncertain states can be used in estimation, planning, control, and safety analysis of stochastic dynamical systems.
1 code implementation • 27 May 2020 • Allen Wang, Xin Huang, Ashkan Jasour, Brian Williams
The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models for predictions of both agent positions and controls.
1 code implementation • 10 May 2020 • Jingwei Song, Mitesh Patel, Ashkan Jasour, Maani Ghaffari
In this paper, we present a statistical inference on the element-wise uncertainty quantification of the estimated deforming 3D shape points in the case of the exact low-rank SDP problem.
no code implementations • 23 Mar 2020 • Allen Wang, Ashkan Jasour, Brian Williams
Chance-constrained motion planning requires uncertainty in dynamics to be propagated into uncertainty in state.
1 code implementation • 31 Jan 2017 • Ashkan Jasour, Constantino Lagoa
In this paper, we generalize the chance optimization problems and introduce constrained volume optimization where enables us to obtain convex formulation for challenging problems in systems and control.
Optimization and Control