Vehicle co-operation could 'radically' cut motorway pile-ups

 

A consortium led by automotive technology firm Applus IDIADA has been awarded government funding to develop technology that could radically cut motorway pile-ups by helping vehicles make cooperative decisions.

The Multi-Car Collision Avoidance (MuCCA) project will use artificial intelligence (AI) and vehicle-to-vehicle communications to help cars and eventually autonomous vehicles to cooperate, reducing multi-car collisions.

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AI and connected vehicle technology will be used to avoid collisions

A trial of the technology will also require the MuCCA system equipped vehicles to use AI methods to predict the likely movements of cars driven by people. If the MuCCA-controlled vehicles cannot avoid an accident altogether, the aim will be to minimise the consequences.

Charlie Wartnaby, chief engineer from IDIADA, said: ‘The beauty of connected vehicles is that they can share and combine sensor data with other vehicles, making them more than the sum of their parts.

‘We can use this ability to allow machine logic to take control of a group of vehicles such that they work together in an emergency to avoid an accident, deciding optimal joint trajectories to avoid complex collisions with both human and machine-driven vehicles in a way that human drivers could not.

‘Even a single MuCCA vehicle will have superlative collision avoidance capability using its 360-degree prediction of human-driven vehicles around it.’

The consortium behind the project also includes Cranfield University, Westfield Sports Cars, Cosworth, Secured by Design and the Transport Systems Catapult.

The project will also develop data logging capabilities to create a record of the exact causes of accidents.

A computer-simulated environment will also be created, in which the vehicles’ AI systems can practise complex crash scenarios before being trialled on real-world test tracks.

The funding comes from a competition from Innovate UK, the Government’s national innovation agency, and the Centre for Connected and Autonomous Vehicles.

 

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