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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.

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

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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