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On the Verification and Validation of AI Navigation Algorithms

arXiv 2101.06091 ↗First posted 15 Jan 2021cs.AIcited by 0

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Abstract (arXiv)

This paper explores the state of the art on to methods to verify and validate navigation algorithms for autonomous surface ships. We perform a systematic mapping study to find research works published in the last 10 years proposing new algorithms for autonomous navigation and collision avoidance and we have extracted what verification and validation approaches have been applied on these algorithms. We observe that most research works use simulations to validate their algorithms. However, these simulations often involve just a few scenarios designed manually. This raises the question if the algorithms have been validated properly. To remedy this, we propose the use of a systematic scenario-based testing approach to validate navigation algorithms extensively.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 144 references that are in the map. First posted on arXiv 15 Jan 2021.

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3 of 144 references.

References · 3

  1. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  2. 2019The Branching-Course MPC Algorithm for Maritime Collision Avoidancecited by 3
  3. 2019Hybrid Collision Avoidance for ASVs Compliant with COLREGs Rules 8 and 13-17cited by 2

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