Scalability of a Machine Learning Environment for Autonomous Driving Research

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

2 Sitaatiot (Scopus)
179 Lataukset (Pure)

Abstrakti

We study scalability of machine learning environments in the context of mixed collaborative driving. Mixed collaborative driving includes both human controlled vehicles and vehicles controlled by AI (Artificial Intelligence) that share the physical road resources (e.g., intersections and roundabouts). Many such driving situations cannot be easily created nor replicated in the real life. Therefore, development and testing of AI systems is often done with simulators.

Machine learning environments must maintain a real-time understanding of their traffic situation. Scaling of the machine learning environment to multiple distributed nodes is required to support larger number of participating vehicles. Our experimental environment consists of the CARLA simulator, custom AI implemented with the TensorFlow framework, and a corner casesearch subsystem. With the corner case search subsystem we can automatically evaluate the AI in different driving scenarios. In this paper, we present how scaling of the envinronment tomultiple distributed nodes affects its performance.
AlkuperäiskieliEnglanti
OtsikkoProceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
KustantajaIEEE
Sivut687-692
Sivumäärä6
ISBN (elektroninen)9781728129273
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Industrial Informatics - Aalto University, Helsinki and Espoo, Suomi
Kesto: 22 heinäk. 201925 heinäk. 2019
Konferenssinumero: 17
https://www.indin2019.org/

Julkaisusarja

NimiIEEE International Conference on Industrial Informatics (INDIN)
KustantajaIEEE
ISSN (painettu)1935-4576

Conference

ConferenceIEEE International Conference on Industrial Informatics
LyhennettäINDIN
Maa/AlueSuomi
KaupunkiHelsinki and Espoo
Ajanjakso22/07/201925/07/2019
www-osoite

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