Zobrazeno 1 - 10
of 149
pro vyhledávání: '"Morton, Jeremy"'
Autor:
Bhattacharyya, Raunak, Wulfe, Blake, Phillips, Derek, Kuefler, Alex, Morton, Jeremy, Senanayake, Ransalu, Kochenderfer, Mykel
An open problem in autonomous vehicle safety validation is building reliable models of human driving behavior in simulation. This work presents an approach to learn neural driving policies from real world driving demonstration data. We model human dr
Externí odkaz:
http://arxiv.org/abs/2006.06412
The computational cost associated with simulating fluid flows can make it infeasible to run many simulations across multiple flow conditions. Building upon concepts from generative modeling, we introduce a new method for learning neural network model
Externí odkaz:
http://arxiv.org/abs/1912.06752
Koopman theory asserts that a nonlinear dynamical system can be mapped to a linear system, where the Koopman operator advances observations of the state forward in time. However, the observable functions that map states to observations are generally
Externí odkaz:
http://arxiv.org/abs/1902.09742
Autor:
Carlberg, Kevin T., Jameson, Antony, Kochenderfer, Mykel J., Morton, Jeremy, Peng, Liqian, Witherden, Freddie D.
Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the number of times the solution is saved to disk, yet retain the ability to recover any field quantity (at any time instan
Externí odkaz:
http://arxiv.org/abs/1812.01177
The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high ac
Externí odkaz:
http://arxiv.org/abs/1805.07472
Autor:
Bhattacharyya, Raunak P., Phillips, Derek J., Wulfe, Blake, Morton, Jeremy, Kuefler, Alex, Kochenderfer, Mykel J.
Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through t
Externí odkaz:
http://arxiv.org/abs/1803.01044
Publikováno v:
In eClinicalMedicine May 2022 47
Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment. Much of this case often relies upon critical event outcomes from real-world testing, requiring manufacturers to be strategic ab
Externí odkaz:
http://arxiv.org/abs/1707.08234
Autor:
Morton, Jeremy, Kochenderfer, Mykel J.
In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between fo
Externí odkaz:
http://arxiv.org/abs/1704.05566
The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts
Externí odkaz:
http://arxiv.org/abs/1701.06699