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Open Access

CPT: A Configurable Predictability Testbed for DNN Inference in AVs

Department of Computer and Information Science, University of Delaware, Newark, DE 19713, USA
Department of Electrical & Computer Engineering, Northeastern University, Boston, MA 02115, USA
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Abstract

Predictability is an essential challenge for autonomous vehicles (AVs)’ safety. Deep neural networks have been widely deployed in the AV’s perception pipeline. However, it is still an open question on how to guarantee the perception predictability for AV because there are millions of deep neural networks (DNNs) model combinations and system configurations when deploying DNNs in AVs. This paper proposes configurable predictability testbed (CPT), a configurable testbed for quantifying the predictability in AV’s perception pipeline. CPT provides flexible configurations of the perception pipeline on data, DNN models, fusion policy, scheduling policies, and predictability metrics. On top of CPT, the researchers can profile and optimize the predictability issue caused by different application and system configurations. CPT has been open-sourced at: https://github.com/Torreskai0722/CPT.

Keywords

predictabilitydeep neural networkautonomous driving

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Tsinghua Science and Technology
Volume 30 Issue 1,
February 2025
Pages 87-99
DOI: 10.26599/TST.2024.9010037
Cite this article:
Liu L, Wang Y, Shi W. CPT: A Configurable Predictability Testbed for DNN Inference in AVs. Tsinghua Science and Technology, 2025, 30(1): 87-99. https://doi.org/10.26599/TST.2024.9010037

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Received: 29 October 2023
Revised: 09 January 2024
Accepted: 07 February 2024
Published: 11 September 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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