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Creating a Formally Verified Neural Network for Autonomous Navigation: An Experience Report

arXiv 2411.14163 ↗First posted 21 Nov 2024cs.LO cs.CV cs.LGcited by 0

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Abstract (arXiv)

The increased reliance of self-driving vehicles on neural networks opens up the challenge of their verification. In this paper we present an experience report, describing a case study which we undertook to explore the design and training of a neural network on a custom dataset for vision-based autonomous navigation. We are particularly interested in the use of machine learning with differentiable logics to obtain networks satisfying basic safety properties by design, guaranteeing the behaviour of the neural network after training. We motivate the choice of a suitable neural network verifier for our purposes and report our observations on the use of neural network verifiers for self-driving systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 21 Nov 2024.

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23 of 47 references.

References · 23

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  3. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  4. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 445
  5. 2020Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifierscited by 187
  6. 2020NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systemscited by 128
  7. 2022General Cutting Planes for Bound-Propagation-Based Neural Network Verificationcited by 103
  8. 2020Analyzing Differentiable Fuzzy Logic Operatorscited by 101
  9. 2020Verification of Deep Convolutional Neural Networks Using ImageStarscited by 75
  10. 2021The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Resultscited by 71
  11. 2024Neural Network Verification with Branch-and-Bound for General Nonlinearitiescited by 47
  12. 2022Deep Learning with Logical Constraintscited by 44
11 more
  1. 2018Formal Verification of Neural Network Controlled Autonomous Systemscited by 41
  2. 2022The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Resultscited by 39
  3. 2023The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Resultscited by 38
  4. 2021Neural Network Robustness as a Verification Property: A Principled Case Studycited by 35
  5. 2023Provably Bounding Neural Network Preimagescited by 29
  6. 2021DNNV: A Framework for Deep Neural Network Verificationcited by 23
  7. 2020Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAIcited by 21
  8. 2019Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controllercited by 17
  9. 2023Logic of Differentiable Logics: Towards a Uniform Semantics of DLcited by 15
  10. 2024Comparing differentiable logics for learning with logical constraintscited by 7
  11. 2022Risk Verification of Stochastic Systems with Neural Network Controllerscited by 6

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