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CONTEXT-AWARE, ENERGY-EFFICIENT, AND TRUSTWORTHY PERCEPTION SYSTEMS FOR CCAM WITH DISTRIBUTED AI AND NEXT-GENERATION SENSORS

Objective

Recent European trials (e.g. Hi-Drive) and early driverless robotaxis deployments have shown that robust environment perception remains a key bottleneck for CCAM: today’s perception stacks still struggle with dense VRU presence, occlusions, and degraded visibility (fog/rain/night/reflective surfaces). Yet improving robustness by adding more sensors and larger models raises compute demand, power consumption, heat, and costs, or shifts the burden to costly roadside infrastructure. A central barrier, therefore, remains: how to deliver reliable and trustworthy perception without unsustainable energy and cost growth?

CONCENTRA proposal aims to overcome this barrier through an integrated set of transformative enablers spanning novel sensing systems, emerging AI models, and novel systems engineering approaches. We will combine next-generation sensors with context-adaptive sensor selection and modular fusion to optimise accuracy-power trade-offs at runtime. Solid-state LiDAR and 4D imaging radar, both featuring embedded AI from our industry partners, will reduce latency in the sense-think-act loop while lowering power consumption and cost relative to spinning LiDAR baselines. Effective runtime integrity monitoring will detect degradations and support timely safe fallback (e.g. remote assistance requests) when alert limits are exceeded, which is crucial for higher levels of autonomy (e.g. Level 3 and 4). Distributed AI optimisation will let resource-limited vehicles benefit from powerful cloud-trained ensembles, while multi-RAT V2X connectivity will provide resilient collective perception with path redundancy. Standardised interfaces for plug-and-play reuse of selected sensors and perception modules will support circularity and cost-effective scaling, complemented by exploratory mapping of sensor-fusion modules to chiplet-based computing. Validation will culminate in physical proof-of-concept demonstrations on public roads and an advanced smart-city site.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

(opens in new window) HORIZON-CL5-2026-01

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Coordinator

THE QUEEN'S UNIVERSITY OF BELFAST
Net EU contribution
€ 835 350,00
Address
UNIVERSITY ROAD LANYON BUILDING
BT7 1NN BELFAST
United Kingdom
Region
Northern Ireland Northern Ireland Belfast
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data

Participants (10)