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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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.4.1 - Widening participation and spreading excellence
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-CSA - HORIZON Coordination and Support Actions
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-WIDERA-2025-02
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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.
58070 Sivas Merkez
Türkiye
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.