Conference paper Open Access
Zenoh-based Dataflow Framework for Autonomous Vehicles
Baldoni, Gabriele;
Loudet, Julien;
Cominardi, Luca;
Corsaro, Angelo;
He, Yong
Autonomous Vehicle Softwares are complex to implement. They have strong security and safety constraints and are at the crossroad of several domains, thus leading to as many independent components. Plus, as foreseen by the Vehicle to Everything paradigm, Autonomous Vehicles are expected to interact with their surroundings, again increasing their overall complexity.
To cope with these requirements, frameworks based on dataflow programming are emerging. Indeed, dataflow programming is particularly suited in this context as it is already being used in robotics and time-critical applications. However, it was not designed with decentralization in mind, hence leaving all communication-related aspects to application developers. To address these challenges, we propose to leverage Eclipse Zenoh, an Edge-native technology. For that purpose, we first show how we used Zenoh to enhance ERDOS, a dataflow framework for Autonomous Vehicles. We then motivate and draft our own dataflow framework based on Zenoh: Zenoh Flow.
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AVS_21___Final___Zenoh_based_data_flow_framework_for_autonomous_vehicles.pdf
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- Publication date:
- November 29, 2021
- DOI:
-
- Keyword(s):
- Dataflow; edge; V2X; automotive; robotics; eclipse zenoh
- Grants:
-
European Commission:
- DAEMON - Network intelligence for aDAptive and sElf-Learning MObile Networks (101017109)
- Meeting:
- IEEE International Symposium on Autonomous Vehicle Software (AVS 2021) (IEEE QRS 2021 AVS), Hainan Island, China, December 6-10, 2021
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International