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Ultra-layered perception with brain-inspired information processing for vehicle collision avoidance

Project information

Grant agreement ID: 778062

Status

Ongoing project

  • Start date

    1 December 2018

  • End date

    30 November 2022

Funded under:

H2020-EU.1.3.3.

  • Overall budget:

    € 2 191 500

  • EU contribution

    € 1 894 500

Coordinated by:

UNIVERSITY OF LINCOLN

United Kingdom

Objective

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.

Field of Science

/engineering and technology/mechanical engineering/vehicle engineering/automotive engineering/autonomous vehicle

/natural sciences/computer and information sciences/data science/data processing

/engineering and technology/electrical engineering, electronic engineering, information engineering/information engineering/telecommunications/radio technology/radar

Programme(s)

H2020-EU.1.3.3. - Stimulating innovation by means of cross-fertilisation of knowledge

Topic(s)

MSCA-RISE-2017 - Research and Innovation Staff Exchange

Call for proposal

H2020-MSCA-RISE-2017

See other projects for this call

Funding Scheme

MSCA-RISE - Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE)
Leaflet | Map data © OpenStreetMap contributors, Credit: EC-GISCO, © EuroGeographics for the administrative boundaries

Coordinator

UNIVERSITY OF LINCOLN

Address

Brayford Pool
Ln6 7ts Lincoln

United Kingdom

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 891 000

Website

Contact the organisation

Participants (6)

UNIVERSITAET HAMBURG

Germany

EU Contribution

€ 162 000

UNIVERSITY OF NEWCASTLE UPON TYNE

United Kingdom

EU Contribution

€ 324 000

WESTFAELISCHE WILHELMS-UNIVERSITAET MUENSTER

Germany

EU Contribution

€ 171 000

VISOMORPHIC TECHNOLOGY LTD

United Kingdom

EU Contribution

€ 58 500

DINO ROBOTICS GMBH

Germany

EU Contribution

€ 9 000

AGILE ROBOTS AG

Germany

EU Contribution

€ 279 000

Partners (11)

TSINGHUA UNIVERSITY

China

XI'AN JIAOTONG UNIVERSITY

China

Huazhong University of Science and Technology

China

NORTHWESTERN POLYTECHNICAL UNIVERSITY

China

UNIVERSIDAD DE BUENOS AIRES

Argentina

NATIONAL UNIVERSITY CORPORATION TOKYO UNIVERSITY OF AGRICULTURE AND TECHNOLOGY

Japan

UNIVERSITI PUTRA MALAYSIA

Malaysia

LINGNAN NORMAL UNIVERSITY

China

GUIZHOU UNIVERSITY

China

INSTITUTE OF AUTOMATION CHINESE ACADEMY OF SCIENCES

China

Guangzhou University

China

Project information

Grant agreement ID: 778062

Status

Ongoing project

  • Start date

    1 December 2018

  • End date

    30 November 2022

Funded under:

H2020-EU.1.3.3.

  • Overall budget:

    € 2 191 500

  • EU contribution

    € 1 894 500

Coordinated by:

UNIVERSITY OF LINCOLN

United Kingdom

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Last update: 16 September 2019

Record number: 216435