Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Yel, Esen"'
Autor:
Delecki, Harrison, Vazquez-Chanlatte, Marcell, Yel, Esen, Wray, Kyle, Arnon, Tomer, Witwicki, Stefan, Kochenderfer, Mykel J.
Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based planners may be brittle under these types of uncertainty because they
Externí odkaz:
http://arxiv.org/abs/2402.09388
For autonomous vehicles to proactively plan safe trajectories and make informed decisions, they must be able to predict the future occupancy states of the local environment. However, common issues with occupancy prediction include predictions where m
Externí odkaz:
http://arxiv.org/abs/2310.01723
Perception systems operate as a subcomponent of the general autonomy stack, and perception system designers often need to optimize performance characteristics while maintaining safety with respect to the overall closed-loop system. For this reason, i
Externí odkaz:
http://arxiv.org/abs/2307.01371
Autor:
Yildiz, Anil, Yel, Esen, Corso, Anthony L., Wray, Kyle H., Witwicki, Stefan J., Kochenderfer, Mykel J.
One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen environments is often very costly and requires substantial data collection, it becomes computati
Externí odkaz:
http://arxiv.org/abs/2305.18633
Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems
Autor:
Rober, Nicholas, Katz, Sydney M., Sidrane, Chelsea, Yel, Esen, Everett, Michael, Kochenderfer, Mykel J., How, Jonathan P.
As neural networks (NNs) become more prevalent in safety-critical applications such as control of vehicles, there is a growing need to certify that systems with NN components are safe. This paper presents a set of backward reachability approaches for
Externí odkaz:
http://arxiv.org/abs/2209.14076
Detection and segmentation of moving obstacles, along with prediction of the future occupancy states of the local environment, are essential for autonomous vehicles to proactively make safe and informed decisions. In this paper, we propose a framewor
Externí odkaz:
http://arxiv.org/abs/2209.13172
This paper addresses a safe planning and control problem for mobile robots operating in communication- and sensor-limited dynamic environments. In this case the robots cannot sense the objects around them and must instead rely on intermittent, extern
Externí odkaz:
http://arxiv.org/abs/2209.04534
Safe and reliable autonomy solutions are a critical component of next-generation intelligent transportation systems. Autonomous vehicles in such systems must reason about complex and dynamic driving scenes in real time and anticipate the behavior of
Externí odkaz:
http://arxiv.org/abs/2207.05228
Autor:
Yel, Esen, Bezzo, Nicola
Due to changes in model dynamics or unexpected disturbances, an autonomous robotic system may experience unforeseen challenges during real-world operations which may affect its safety and intended behavior: in particular actuator and system failures
Externí odkaz:
http://arxiv.org/abs/2104.15081
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