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Fast Liquid-neUral Intelligent Driver for CARs

Objective

FLUIDCAR develops a low-power, explainable Liquid Neural Network (LNN) that learns human steering, throttle, and braking from real driving—with no autonomous actuation. The model runs in shadow-mode: a human drives; from camera frames plus CAN/IMU context the LNN proposes control signals; we log discrepancies (e.g. steering-angle RMSE) for training and evaluation. A simple driver-ID button (1 = PI, 2 = co-driver) enables personalised models; we train two LNNs and quantify cross-driver transfer (own-model vs other-model) to measure generalisation.
Technically, we deliver a closed-form continuous-time LNN (≤2M params) optimised for edge inference <10 ms and ≤5 J per inference on Jetson Orin NX via INT8/TensorRT. Robustness is built through a three-loop process: causal saliency audits, a 300-scenario simulation stress-lab (weather, glare, sensor dropouts), and public-road shadow-mode campaigns across urban/rural, day/night, and adverse weather. Video is cached on-board, securely uploaded to HPC for training, then deleted; only models, CAN/IMU logs, and FAIR metadata are retained under GDPR-aligned procedures. Explainability comes from neuron-level saliency and full replay of high-error events; we run an ISO 26262 orientation gap-analysis to ease later OEM integration.
Outcomes include a licensable Driver-Mimic Evaluation Kit (sensor harness + edge engine + SDK), open research code and models, a cross-driver evaluation report, and an investor-ready business plan. After the project, we will sell the know-how and software to the automotive sector (Tier-1 suppliers and OEM advanced-engineering teams), with a pathway to personalised driver-twin analytics and future integration into production ADAS stacks.

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Topic(s)

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HORIZON-CSA - HORIZON Coordination and Support Actions

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Call for proposal

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(opens in new window) HORIZON-WIDERA-2025-02

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Coordinator

Ada Guzey Muhendislik Yazilim Mekatronik Sanayi Ve Ticaret Limited Sirketi
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 391 400,00
Address
Yenisehir, Kardesler Cd. No: 7/2 D:B Blok
58070 Sivas Merkez
Türkiye

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Orta Anadolu Kayseri Sivas
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
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Total cost

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No data