Search Results for author: Anthony L. Corso

Found 5 papers, 2 papers with code

Efficient Determination of Safety Requirements for Perception Systems

no code implementations3 Jul 2023 Sydney M. Katz, Anthony L. Corso, Esen Yel, Mykel J. Kochenderfer

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.

Gaussian Processes

AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator

1 code implementation19 Jun 2023 Elysia Q. Smyers, Sydney M. Katz, Anthony L. Corso, Mykel J. Kochenderfer

We also provide an interface that evaluates trained models on slices of this dataset to identify changes in performance with respect to changing environmental conditions.

object-detection Object Detection

Risk-Driven Design of Perception Systems

1 code implementation21 May 2022 Anthony L. Corso, Sydney M. Katz, Craig Innes, Xin Du, Subramanian Ramamoorthy, Mykel J. Kochenderfer

We formulate a risk function to quantify the effect of a given perceptual error on overall safety, and show how we can use it to design safer perception systems by including a risk-dependent term in the loss function and generating training data in risk-sensitive regions.

ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs

no code implementations9 Jun 2021 Christopher A. Strong, Sydney M. Katz, Anthony L. Corso, Mykel J. Kochenderfer

We demonstrate how to formulate and solve three types of optimization problems: (i) minimization of any convex function over the output space, (ii) minimization of a convex function over the output of two networks in series with an adversarial perturbation in the layer between them, and (iii) maximization of the difference in output between two networks.

Verification of Image-based Neural Network Controllers Using Generative Models

no code implementations14 May 2021 Sydney M. Katz, Anthony L. Corso, Christopher A. Strong, Mykel J. Kochenderfer

For this reason, recent work has focused on combining techniques in formal methods and reachability analysis to obtain guarantees on the closed-loop performance of neural network controllers.

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