Partners: DRISK.AI LTD (Lead), IMPERIAL COLLEGE LONDON, CLAYTEX SERVICES LIMITED, DG CITIES LIMITED, RFPRO LIMITED. 

To train autonomous vehicles to safely navigate ‘edge case’ situations, they have to be identified and simulated.

Part of the team’s previous project, DRISK focused on engagement, using the public’s driving experiences to crowd source these rare events and build scenarios – a process that also served to raise awareness of the technology and give an insight into consumer barriers to autonomy. 

Real-life training data for autonomous vehicles is limited. There is a widely recognised need for simulation and synthetic data, but the artificial data currently available is not sufficiently ‘sensor-real’ for AI trained by simulation to improve its perception and decision-making. DeepSafe brings together leaders in the simulation supply chain to resolve these synthetic data issues to enable the successful simulation-based training vital to develop safe, reliable self-driving services.

In addition, DeepSafe will establish the definitive toolchain to realistically represent the dynamics of vulnerable road users, and will conclusively answer ‘how close does simulation have to be?’ to train an ADS (Automated Driving System) to outperform a human driver by an order of magnitude.

Balazs Csuvar, Head of Delivery at DG Cities says:

A lot of our focus within the consortium is on user perception – how can we make sure that people feel safe in self-driving cars, how can they be reassured by and trust the work that has gone into the testing. Lane-keeping assist and other ADAS features (in some way precursors to fully autonomous driving) already influence the way we drive, without people actually knowing how well they perform. We will work on understanding how best to communicate to drivers this crucial information and use it to outline how autonomous systems should be benchmarked as well.”