Enabling unit testing of already-integrated AI software systems: The case of Apollo for autonomous driving

dc.contributor.authorAlcón Doganoc, Miguel
dc.contributor.authorTabani, Hamid
dc.contributor.authorAbella Ferrer, Jaume
dc.contributor.authorCazorla Almeida, Francisco Javier
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2021-10-14T13:56:38Z
dc.date.available2021-10-14T13:56:38Z
dc.date.issued2021
dc.description.abstractThe advanced AI-based software used for autonomous driving comprises multiple highly-coupled modules that are data and control dependent. Deploying those already-integrated software frameworks makes unit testing, a fundamental step in the validation process of critical software, very challenging in safety-critical systems. To tackle this issue, in this paper, we show the steps we followed to develop standalone versions of the modules in an industry-level autonomous driving framework (Apollo) by applying several modifications to its architectural design. We show how the standalone modules have the same functional behavior as their integrated counterpart modules. We exemplify the benefits of standalone modules by performing incremental analysis of the software timing requirements of each module running on a heterogeneous System on Chip (SoC). This is a mandatory step to consolidate and integrate software modules guaranteeing timing constraints (e.g. related to freedom from interference) while maximizing SoC utilization.
dc.description.peerreviewedPeer Reviewed
dc.description.sponsorshipThis work has been partially supported by the Spanish Ministry of Science, Innovation and Universities under grant PID2019-110854RB-I00/AEI/10.13039/501100011033, the UP2DATE European Union’s Horizon 2020 (H2020) research and innovation programme under grant agreement No 871465, the SuPerCom European Research Council (ERC) project under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773), and the HiPEAC Network of Excellence.
dc.description.versionPostprint (author's final draft)
dc.format.extent8 p.
dc.identifier.citationAlcón, M. [et al.]. Enabling unit testing of already-integrated AI software systems: The case of Apollo for autonomous driving. A: Euromicro Conference on Digital System Design. "2021 24th Euromicro Conference on Digital System Design, DSD 2021: virtual conference, 1-3 September 2021: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 426-433. ISBN 978-1-6654-2703-6. DOI 10.1109/DSD53832.2021.00071.
dc.identifier.doi10.1109/DSD53832.2021.00071
dc.identifier.isbn978-1-6654-2703-6
dc.identifier.urihttps://hdl.handle.net/2117/353609
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/871465/EU/Intelligent software-UPDATE technologies for safe and secure mixed-criticality and high performance cyber physical systems/UP2DATE
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/772773/EU/Sustainable Performance for High-Performance Embedded Computing Systems/SuPerCom
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9556323
dc.rights.accessOpen Access
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshSystems on a chip
dc.subject.lcshAutonomous vehicles
dc.subject.lemacSistemes monoxip
dc.subject.lemacVehicles autònoms
dc.subject.otherUnit testing
dc.subject.otherAutonomous driving
dc.subject.otherApollo
dc.titleEnabling unit testing of already-integrated AI software systems: The case of Apollo for autonomous driving
dc.typeConference report
dspace.entity.typePublication
local.citation.authorAlcón, M.; Tabani, H.; Abella, J.; Cazorla, F. J.
local.citation.contributorEuromicro Conference on Digital System Design
local.citation.endingPage433
local.citation.publicationName2021 24th Euromicro Conference on Digital System Design, DSD 2021: virtual conference, 1-3 September 2021: proceedings
local.citation.startingPage426
local.identifier.drac32109834

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