Skip to main content
European Commission logo print header

Life-like visual information processing for robust collision detection


To be able to detected collision efficiently is of vital importance for the survival of animals that are migrating at speed, especially for those flying in dense swarms like locusts. Vision plays a critical role in collision detection for most animal species in a dynamic world. It is expected that in future, many human made machines, such like ground vehicles, mobile robots, and unmanned aerial vehicles, should all be able to detect and avoid collisions effectively as animals do. The challenge to achieve this is huge. Biological visual neural systems provide ideal models to achieve this goal.

LIVCODE consortium focuses on robust solutions for visual based collision detection. Taking the inspiration from biological visual systems, the consortium will bring neurobiologists, neural system modellers, chip designers, and robotic researchers together and complement each others’ research strengths via staff secondments, and jointly organised seminars and workshops. The consortium will investigate robust solutions for collision detection in the real world, through neural system modelling, neural model integration, chip realization and application, in order to build strong connections between the European institutions and partner institutions in a fast growing economy.

Six work packages (WPs) are designed to achieve the objectives of the project:

WP0: Project management,
WP1: Biological plausible visual neural system modelling,
WP2: Multiple visual neural systems integration,
WP3: VLSI neural vision chip design,
WP4: Neural vision systems for mobile robots and unmanned aerial systems, and
WP5: Dissemination, exploitation, business model.

Call for proposal

See other projects for this call


Brayford pool
LN6 7TS Lincoln
United Kingdom

See on map

East Midlands (England) Lincolnshire Lincolnshire
Activity type
Higher or Secondary Education Establishments
Administrative Contact
Shigang Yue (Dr.)
EU contribution
No data

Participants (2)