Scalability of a Machine Learning Environment for Autonomous Driving Research

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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.
Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
Number of pages6
ISBN (Electronic)9781728129273
Publication statusPublished - 2019
MoE publication typeA4 Conference publication
EventIEEE International Conference on Industrial Informatics - Aalto University, Helsinki and Espoo, Finland
Duration: 22 Jul 201925 Jul 2019
Conference number: 17

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
ISSN (Print)1935-4576


ConferenceIEEE International Conference on Industrial Informatics
Abbreviated titleINDIN
CityHelsinki and Espoo
Internet address


  • Simulation
  • Hybrid Systems
  • New Control Applications


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