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H2020Staff exchange2018–2024

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

Horizon 2020 — Marie Skłodowska-Curie Actions

Duration
2018-12-01 → 2024-09-30
EU contribution
€1,894,500
Participants
19
Scheme
MSCA-RISE

Lines connect the coordinator with its partners.

Results in brief

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

Although in their early stages, autonomous vehicles, have demonstrated huge potential in shaping future lifestyles. However, to be accepted by ordinary users, autonomous vehicles have a critical issue to solve – being trustworthy at collision detection. Autonomous vehicles that experience accidents once every few months or years would be unacceptable to the general public. In the real world, human driven vehicles collide 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 does not work well on absorbing/reflective surfaces, GPS based methods are difficult in cities with tall 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 make autonomous vehicles safe to serve human society, a new type of trustworthy, robust, low-cost, and low energy consumption vehicle collision detection and avoidance systems are needed. This ULTRACEPT consortium proposes an innovative solution with brain-inspired multiple layered and multiple modalities information processing for trustworthy vehicle collision detection. Connecting multidisciplinary teams from different countries together via staff exchange and collaboration, 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.

Data: CORDIS, © European Union

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.

Original text from CORDIS.

Participants

  • UNIVERSITY OF LEICESTER · LeicesterCoordinatorUnited Kingdom
  • AGILE ROBOTS AG · MUNCHENGermany
  • DINO ROBOTICS GMBH · KARLSRUHEGermany
  • GUANGZHOU UNIVERSITY · GUANGZHOUChina
  • GUIZHOU UNIVERSITY · GuiyangChina
  • HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY · WUHANChina
  • INSTITUTE OF AUTOMATION CHINESE ACADEMY OF SCIENCES · BEIJINGChina
  • LINGNAN NORMAL UNIVERSITY · ZHANJIANG GUANGDONGChina
  • NATIONAL UNIVERSITY CORPORATION TOKYO UNIVERSITY OF AGRICULTURE AND TECHNOLOGY · Fuchu Shi TokyoJapan
  • NORTHWESTERN POLYTECHNICAL UNIVERSITY · XI ANChina
  • TSINGHUA UNIVERSITY · BEIJINGChina
  • UNIVERSIDAD DE BUENOS AIRES · Buenos AiresArgentina
  • UNIVERSITAET MUENSTER · MuensterGermany
  • UNIVERSITI PUTRA MALAYSIA · Selangor Darul EhsanMalaysia
  • UNIVERSITY OF HAMBURG · HamburgGermany
  • UNIVERSITY OF LINCOLN · LincolnUnited Kingdom
  • UNIVERSITY OF NEWCASTLE UPON TYNE · Newcastle Upon TyneUnited Kingdom
  • VISOMORPHIC TECHNOLOGY LTD · LondonUnited Kingdom
  • XI'AN JIAOTONG UNIVERSITY · XI'ANChina

Links

Data: CORDIS, © European Union