Explore the words cloud of the ULTRACEPT project. It provides you a very rough idea of what is the project "ULTRACEPT" about.
The following table provides information about the project.
UNIVERSITY OF LINCOLN
|Coordinator Country||United Kingdom [UK]|
|Total cost||2˙191˙500 €|
|EC max contribution||1˙894˙500 € (86%)|
1. H2020-EU.1.3.3. (Stimulating innovation by means of cross-fertilisation of knowledge)
|Duration (year-month-day)||from 2018-12-01 to 2022-11-30|
Take a look of project's partnership.
|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|
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.
|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.
|year||authors and title||journal||last update|
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|
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|
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|
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|
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.