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ULTRACEPT SIGNED

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

Total Cost €

EC-Contrib. €

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.

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

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

Leaflet | Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox

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

List of publications.
year	authors and title	journal	last update
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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