Discovering Closed-Loop Failures of Vision-Based Controllers via Reachability Analysis

Author:

Chakraborty Kaustav1ORCID,Bansal Somil1

Affiliation:

1. Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Reference27 articles.

1. Verifying controllers with convolutional neural network-based perception: A case for intelligible, safe, and precise abstractions;hsieh,2021

2. Guaranteeing Safety for Neural Network-Based Aircraft Collision Avoidance Systems

3. Laminar research: X-plane 11 (2019),2019

4. Nnlander-verif: A neural network formal verification framework for vision-based autonomous aircraft landing;cruz;Proc NASA Formal Methods Symp,0

5. Output Reachable Set Estimation and Verification for Multilayer Neural Networks

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1. Bridging Dimensions: Confident Reachability for High-Dimensional Controllers;Lecture Notes in Computer Science;2024-09-11

2. Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. Introduction to the Special Issue on Automotive CPS Safety & Security: Part 2;ACM Transactions on Cyber-Physical Systems;2024-04-30

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