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Main sources of variability and non-determinism in AD software: taxonomy and prospects to handle them

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Abstract

Safety standards in domains like automotive and avionics seek for deterministic execution (lack of jittery behavior) as a stepping stone to build a certification argument on the correct timing behavior of the system. However, the use of artificial-intelligence (AI) software in safety-critical systems carries several built-in and derivative sources of non-determinism that are at odds with safety standard determinism requirements. In this work we analyze the main sources of non-determinism of autonomous driving (AD) software, as highly representative and compelling example of the use of AI software, deep neural networks (DNN) in particular, in critical embedded systems. Paradoxically, DNN-based software in its inference phase—once the NN structure and weights have been fixed—turns out to consist mainly in matrix multiplications, which are inherently quite time deterministic. Our work focuses on sources of variability and non-determinism in AD software, covering algorithmic elements of AD software, low-level software and hardware computing platform, and data-flow constraints among AD modules. As final contribution of our work, which mainly focuses on problem identification, we develop some prospects on the information and metrics needed to better understand and control the unpredictability and non-determinism of AD software.

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Data availability statement

The data supporting the results reported in this work are available from the authors upon reasonable request.

Notes

  1. We use the terms Deep Learning (DL) and Neural Network (NN) indistinctly.

  2. Note that the standard notation in probability p denotes that it is a probability mass function, while P is a probability measure.

  3. Note that while the impact of such scenario depends on the application semantics, it is arising without breaking P timing bounds.

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Funding

This work has been supported by the Spanish Ministry of Science and Innovation under grant PID2019-107255GBC21/AEI/10.13039/501100011033, and the European Research Council (ERC) grant agreement No. 772773 (SuPerCom).

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Authors and Affiliations

  1. Barcelona Supercomputing Center, Barcelona, Spain

    Miguel Alcon, Axel Brando, Enrico Mezzetti, Jaume Abella & Francisco J. Cazorla

  2. Universitat Politecnica de Catalunya, Barcelona, Spain

    Miguel Alcon

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  1. Miguel Alcon
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Correspondence to Miguel Alcon.

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Alcon, M., Brando, A., Mezzetti, E. et al. Main sources of variability and non-determinism in AD software: taxonomy and prospects to handle them. Real-Time Syst 59, 438–478 (2023). https://doi.org/10.1007/s11241-023-09405-1

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