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ULTRACEPT SIGNED

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

Total Cost €

0

EC-Contrib. €

0

Partnership

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 ULTRACEPT project word cloud

Explore the words cloud of the ULTRACEPT project. It provides you a very rough idea of what is the project "ULTRACEPT" about.

likes    objects    modalities    dim    capacity    vehicle    reliability    radar    spatial    safer    miniaturized    acceptable    too    few    advantages    autonomous    material    night    absorbing    environment    huge    cues    million    segmentation    rain    cities    laser    killed    road    bio    innovative    single    consumption    ordinary    size    visual    vision    human    temporal    lidar    detect    difficult    inspired    neural    thirsty    parallel    society    avoidance    people    trustworthy    serve    layered    buildings    issue    brain    doomed    sensitive    sensors    months    metallic    life    inputs    normal    weather    accident    once    car    computing    styles    proposes    lighting    data    badly    stage    demonstrated    detection    accidents    accepted    pedestrians    shaping    multiple    critical    power    solution    unconnected    low    energy    save    gps    surfaces    takes    expensive    collision    communication    reflective    fog    world    extracting    vehicles    lives    cope    happen   

Project "ULTRACEPT" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY OF LINCOLN 

Organization address
address: Brayford Pool
city: LINCOLN
postcode: LN6 7TS
website: www.lincoln.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 2˙191˙500 €
 EC max contribution 1˙894˙500 € (86%)
 Programme 1. H2020-EU.1.3.3. (Stimulating innovation by means of cross-fertilisation of knowledge)
 Code Call H2020-MSCA-RISE-2017
 Funding Scheme MSCA-RISE
 Starting year 2018
 Duration (year-month-day) from 2018-12-01   to  2022-11-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF LINCOLN UK (LINCOLN) coordinator 891˙000.00
2    UNIVERSITY OF NEWCASTLE UPON TYNE UK (NEWCASTLE UPON TYNE) participant 324˙000.00
3    AGILE ROBOTS AG DE (MUNCHEN) participant 279˙000.00
4    WESTFAELISCHE WILHELMS-UNIVERSITAET MUENSTER DE (MUENSTER) participant 171˙000.00
5    UNIVERSITAET HAMBURG DE (HAMBURG) participant 162˙000.00
6    VISOMORPHIC TECHNOLOGY LTD UK (LONDON) participant 58˙500.00
7    DINO ROBOTICS GMBH DE (KARLSRUHE) participant 9˙000.00
8    Guangzhou University CN (GUANGZHOU) partner 0.00
9    GUIZHOU UNIVERSITY CN (Guiyang) partner 0.00
10    HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY CN (WUHAN) partner 0.00
11    INSTITUTE OF AUTOMATION CHINESE ACADEMY OF SCIENCES CN (BEIJING) partner 0.00
12    LINGNAN NORMAL UNIVERSITY CN (ZHANJIANG GUANGDONG) partner 0.00
13    NATIONAL UNIVERSITY CORPORATION TOKYO UNIVERSITY OF AGRICULTURE AND TECHNOLOGY JP (FUCHU SHI TOKYO) partner 0.00
14    NORTHWESTERN POLYTECHNICAL UNIVERSITY CN (XI AN) partner 0.00
15    TSINGHUA UNIVERSITY CN (BEIJING) partner 0.00
16    UNIVERSIDAD DE BUENOS AIRES AR (BUENOS AIRES) partner 0.00
17    UNIVERSITI PUTRA MALAYSIA MY (SELANGOR DARUL EHSAN) partner 0.00
18    XI'AN JIAOTONG UNIVERSITY CN (XI'AN) partner 0.00

Map

 Project 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.

 Deliverables

List of deliverables.
Preliminary visual neural system models for collision cues extraction Documents, reports 2020-03-06 15:56:46
Database for verification Other 2020-03-06 15:56:43
Project website Websites, patent fillings, videos etc. 2020-02-07 12:43:54

Take a look to the deliverables list in detail:  detailed list of ULTRACEPT deliverables.

 Publications

year authors and title journal last update
List of publications.
2020 Jin Xiao, Yuhang Tian, Ling Xie, Xiaoyi Jiang, Jing Huang
A Hybrid Classification Framework Based on Clustering
published pages: 2177-2188, ISSN: 1551-3203, DOI: 10.1109/tii.2019.2933675
IEEE Transactions on Industrial Informatics 16/4 2020-03-05
2019 Qinbing Fu, Hongxin Wang, Cheng Hu, Shigang Yue
Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review
published pages: 263-311, ISSN: 1064-5462, DOI: 10.1162/artl_a_00297
Artificial Life 25/3 2020-03-05
2019 Qinbing Fu, Cheng Hu, Jigen Peng, F. Claire Rind, Shigang Yue
A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects
published pages: 1-15, ISSN: 2168-2267, DOI: 10.1109/tcyb.2019.2946090
IEEE Transactions on Cybernetics 2019-12-17
2019 Daqi Liu, Nicola Bellotto, Shigang Yue
Deep Spiking Neural Network for Video-Based Disguise Face Recognition Based on Dynamic Facial Movements
published pages: 1-10, ISSN: 2162-237X, DOI: 10.1109/tnnls.2019.2927274
IEEE Transactions on Neural Networks and Learning Systems 19 July 2019 2019-12-16
2019 Hongxin Wang, Jigen Peng, Xuqiang Zheng, Shigang Yue
A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds
published pages: 1-15, ISSN: 2162-237X, DOI: 10.1109/TNNLS.2019.2910418
IEEE Transactions on Neural Networks and Learning Systems 01 May 2019 2019-12-16

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The information about "ULTRACEPT" are provided by the European Opendata Portal: CORDIS opendata.

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