DeepSafe
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.”
More Supply Chain Innovators
Aims to enable accurate representation of ADS sensors in simulation.
Focuses on creating an autonomous dolly for airside cargo movements.
Aims to develop an AV capable of safely driving in residential, urban, and rural environments.
A fully redundant, fail-operational Drive-by-Wire technology platform to enable safe driver-out, on-road autonomous vehicle capability.
Focuses on the development of a modular dual redundant steer-by-wire system for heavily automated and electric vehicles.
A collaborative initiative to create an affordable, robust navigation system for automated vehicles.
Aims to develop advanced position and navigation sensors that work reliably in various environments.
Provides a toolset that helps to efficiently identify, define and execute the test requirements for an ADS.
A safety assurance framework for the safe deployment of AI in self-driving technology across all driving domains.
A ‘plug-and-play’ roadside connectivity solution.
Aims to develop a high-performance imaging radar product specifically designed for AVs.
Aims to deliver a universal and affordable drive-by-wire system that replaces traditional mechanical linkages with electronic ones.
Talk to us
To learn more about the CAM Supply Chain UK competition and the remarkable projects that have been awarded, contact competitionsupport@zenzic.io
{{ teamMember.name }}
{{ teamMember.title }}