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Connected cars project aims to end motorway pile-ups

29 September 2017

Government funding towards the development and trial of technology that could radically reduce the number of multi-car collisions on motorways is awarded to an Applus IDIADA-led consortium, including Cranfield University, Westfield Sports Cars, Cosworth, Secured by Design and the Transport Systems Catapult. The Multi-Car Collision Avoidance (MuCCA) project will use Artificial Intelligence (AI) and vehicle-to-vehicle communications to help cars and eventually autonomous vehicles make cooperative decisions to avoid a potential accident.

The trial will also require the MuCCA system equipped vehicles to predict the likely movements of cars controlled by human drivers using AI methods. If the MuCCA-controlled vehicles cannot avoid an accident altogether, the aim will be to minimise the consequences.

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 is the result of a competition from Innovate UK, the Government’s national innovation agency, and the Centre for Connected and Autonomous Vehicles.

Charlie Wartnaby, Chief Engineer from IDIADA explained:

“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.”

Currently around 5,500 accidents happen per year on UK motorways, contributing to over 1,730 annual deaths and over 22,000 serious injuries on all roads. Incidents on the motorway network also cause delays and congestions which can have a serious economic impact on UK businesses, costing around £21 billion a year according to recent estimates.

Charlie Wartnaby continued: “Connected and Autonomous Vehicle technology offers us an opportunity to work towards the elimination of serious accidents on our roads, saving lives and easing congestion. In this project, we will aim to show exactly how this can be done, whilst taking us another step closer to fully autonomous cars.”

Principal Technologist, Alan Peters from the Transport Systems Catapult said:

“Connected and automated vehicle technology holds a great deal of promise for addressing many of the issues faced by our road networks. The MuCCA project will address the most important – saving lives. As such, we are really excited to be involved, and will seek to use our extensive experience with autonomous technology to help the consortium develop a ground-breaking system and establish the UK as a world leader in the development of connected and automated vehicle solutions.”

Pio Szyjanowicz, Cosworth Head of Automotive Electronics said,

“Cosworth is actively involved in the development of black box recorders for autonomous vehicles. We are proud to be a partner in the pioneering MuCCA project, which will use our data logging technology to help to improve safety and congestion on the roads, while acting as an independent bridge between the automotive and insurance industries.”

Professor Aouf from Cranfield University said,

“Connected and Autonomous Vehicle technology is the guarantee of making car traffic safer in the future. It is also an opportunity for the scientists to propose new solutions to meet the technical autonomy requirements of these cars. MuCCA project is a very exciting opportunity for us to develop innovative machine learning and decision-making techniques for the new generation of cooperative autonomous cars.”

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