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ULTRACEPT

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Ultra layered perception with brain inspired information processing for vehicle...

Ultra layered perception with brain inspired information processing for vehicle collision avoidance Autonomous vehicles, although in its early stage, have demonstrated huge potential in shaping future life styles to many of us. However, to be accepted by ordinary users, autonomous vehicles have a critical issue to solve – this i... ver más

30/09/2024

ULEIC

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITY OF LEICESTER No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 16 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-09-30

MSCA-RISE-2017: Research and Innovation Staff Excha... Objective:The RISE scheme will promote international and inter-sector collaboration through research...

I+D

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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No hay información privada compartida para este proyecto. Habla con el coordinador.

16 Participantes

ULEIC

148.50K€ | Lider

UHAM

162.00K€ | Participante

INTBAU

58.50K€ | Participante

UNIVERSIDAD DE BUENOS AIRES

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Duración del proyecto: 82 meses Fecha Inicio: 2017-11-15
Fecha Fin: 2024-09-30

Líder del proyecto

UNIVERSITY OF LEICESTER No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Autonomous vehicles, although in its early stage, have demonstrated huge potential in shaping future life styles to many of us. However, to be accepted by ordinary users, autonomous vehicles have a critical issue to solve – this is trustworthy collision detection. No one likes an autonomous car that is doomed to a collision accident once every few years or months. In the real world, collision does happen at every second - more than 1.3 million people are killed by road accidents every single year. The current approaches for vehicle collision detection such as vehicle to vehicle communication, radar, laser based Lidar and GPS are far from acceptable in terms of reliability, cost, energy consumption and size. For example, radar is too sensitive to metallic material, Lidar is too expensive and it does not work well on absorbing/reflective surfaces, GPS based methods are difficult in cities with high buildings, vehicle to vehicle communication cannot detect pedestrians or any objects unconnected, segmentation based vision methods are too computing power thirsty to be miniaturized, and normal vision sensors cannot cope with fog, rain and dim environment at night. To save people’s lives and to make autonomous vehicles safer to serve human society, a new type of trustworthy, robust, low cost, and low energy consumption vehicle collision detection and avoidance systems are badly needed. This consortium proposes an innovative solution with brain-inspired multiple layered and multiple modalities information processing for trustworthy vehicle collision detection. It takes the advantages of low cost spatial-temporal and parallel computing capacity of bio-inspired visual neural systems and multiple modalities data inputs in extracting potential collision cues at complex weather and lighting conditions.

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