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Quantifying Safety of Learning-based Self-Driving Control Using
  Almost-Barrier Functions

Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions

28 July 2022
Zhizhen Qin
Tsui-Wei Weng
Sicun Gao
ArXivPDFHTML

Papers citing "Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions"

5 / 5 papers shown
Title
SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation
SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation
Benjamin Stoler
Ingrid Navarro
Jonathan M Francis
Jean Oh
AAML
60
4
0
16 Sep 2024
Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey
Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey
Milan Ganai
Sicun Gao
Sylvia Herbert
40
6
0
12 Jul 2024
Physical Deep Reinforcement Learning Towards Safety Guarantee
Physical Deep Reinforcement Learning Towards Safety Guarantee
H. Cao
Y. Mao
L. Sha
Marco Caccamo
AI4CE
16
5
0
29 Mar 2023
CNN-Cert: An Efficient Framework for Certifying Robustness of
  Convolutional Neural Networks
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Akhilan Boopathy
Tsui-Wei Weng
Pin-Yu Chen
Sijia Liu
Luca Daniel
AAML
108
138
0
29 Nov 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
249
1,838
0
03 Feb 2017
1