The EVES project is developing advanced simulation tools to help autonomous vehicle (AV) developers improve both safety and energy efficiency of AVs. Using cutting-edge AI, the platform generates realistic, location-specific driving scenarios and integrates energy consumption modelling. This allows engineers to test autonomous driving systems under diverse conditions while also understanding the impact of different sensor and computing setups on vehicle range and energy efficiency. By enabling safe, scalable testing in virtual environments, EVES-project supports the design of lighter, more resource-efficient AVs without compromising safety.

Fidelis AI finds self-driving vehicle technology to be opaque, not sufficiently marketed nor efficiently promoted. Most vehicle manufacturers offer 15-30-minute test drive sessions. This may be sufficient to test entertainment and driving comfort features. However, potential customers require significantly more time to thoroughly test self-driving features and assess their safety. The absence of adequate testing opportunities may lead many potential customers to refrain from purchasing self-driving technologies, particularly given the significant additional costs associated with them. Fidelis AI believes that allowing customers more time to test self-driving technology could reverse this trend. To make it economically viable for vehicle manufacturers, this additional testing could be conducted in digital environments. The public funding provided by the TEASE project will greatly accelerate the development of digital technologies for customer testing of self-driving vehicles in safe virtual environments. Overall, this will adjust the levels of trustworthiness drivers have in self-driving technology before purchasing, leading to more informed and responsible owners of self-driving vehicles. This creates greater safety for the public. This vision is in line with the key objectives of the central Government. The project will develop and demonstrate a virtual customer testing tool in the form of a game for self-driving vehicles.