Modelling, Simulations, AI and AR for CAVs Competition Briefing Event - Edinburgh
In the recently published Industrial Strategy, the Government outlined its ambition to have self-driving vehicles on the UK roads by 2021. Today, the UK Government has just agreed on a landmark Automotive Sector Deal which offered details on the next stages of funding for the CAV world.
The first of 2 competitions dedicated to Modelling, Simulations, AI and AR for Connected and Automated Vehicles will offer £15m to industry-led R&D projects that use holistic simulation and modelling systems to develop approval mechanisms and standards for connected and autonomous vehicles.
The KTN would like to invite you to this Briefing Event where you can hear more about the scope for this £15m competition and the application process, as well as networking with representatives of the Automotive, Modelling, Simulations, AI, Augmented Reality and Gaming worlds.
Furthermore, there will be pitching opportunities for those that would like to showcase their work and approach in order to facilitate better networking opportunities. If you would like to be involved, please select this option on the registration form.
Details of the programme will be published in due course.
Background: For CAVs to be part of any future transport system, they need to perform under the same variety and unpredictability of circumstances that human drivers can currently manage and, in the long term, outperform humans in the driving task itself. This means that a CAVs Autonomous Driving System (ADS), the software that makes driving decision based on its interpretation of sensor inputs, needs to make the right decision for each situation it encounters.
Using physical development and testing alone is unpractical, given the number of years and millions of miles on the road it would take to build confidence in these vehicles.
Beyond the real life testing, a high consideration needs to be given to virtual environments and simulations as a valuable complementary solution given their strengths:
- Their versatility
- They can safely test dangerous, uncommon or unlikely scenarios
- They can create their own weather and climate
- They allow for the testing of a high number of scenarios and “driven miles” in a short time.