An AECOM-led consortium has secured more than £4.2 million of funding from Innovate UK and the Centre for Connected & Autonomous Vehicles (CCAV) to deliver a pilot scheme that could pave the way for the use of connected and autonomous vehicles to move people around airports, hospitals, business parks, shopping and tourist centers. The pilot project includes the design, development and testing of new autonomous and connected pods on-demand (PODs), culminating in on-road public trials at London’s Queen Elizabeth Olympic Park.

Comprising 20 partnering organizations, the AECOM-led CAPRI consortium brings together academic institutions, businesses, SMEs and public sector authorities with a range of skills, knowledge and needs in the connected and autonomous vehicles (CAVs) arena. The project will include the development of the next generation of PODs, as well as the systems and technologies that will allow the vehicles to navigate safely and seamlessly in both pedestrian and road environments.

The project includes four trials, with the first on private land at Filton Airfield near Bristol where consortium member YTL is developing a major new community. The aim of this trial will be to test and validate the performance of the new generation PODs. The second trial will test a public service in a shopping center car park to assess performance in busy pedestrian areas. The final two trials will be at the Queen Elizabeth Olympic Park, a large and diverse estate. The first of these trials will test a public on-demand mobility service in pedestrian areas, with the PODs identifying and navigating the best routes. The final public trial will test the PODs on a network of roads around the park, with the service interacting with traffic control systems.

An important aspect of the scheme will be safety and security. For the first time, the project will apply accidentology analysis to PODs to identify potential causes of accidents that will require testing and evaluation, while using state-of-the-art techniques to simulate other scenarios, therefore reducing the need for real-world testing.