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The third edition of the challenge comes with a new competitive theme focused on self-driving buses. Image Credit: Supplied

Dubai: Roads and Transport Authority (RTA) of Dubai has announced August 31 as the deadline for registration for the 3rd Dubai World Challenge for Self-Driving Transport 2023.

The authority has called on companies and local academia to register on time and compete for a share of the prizemoney of $2,300,000.

Ahmed Hashem Bahrozyan, CEO of Public Transport Agency, Chairman of the Organising Committee of the Dubai World Challenge for Self-Driving Transport, RTA, said, “The third edition of the event is focused on self-driving buses. RTA started receiving applications for participation in the event several months ago. Setting August 31 as a deadline that aims to prompt and encourage industry leaders and local academia to expedite their participation through the website.

“The launch of the third edition of the challenge follows the successes of the first two editions. It is part of RTA’s efforts to enhance the high standing of Dubai in self-driving transport. It also echoes RTA’s commitment to realising the government’s vision to transform 25 per cent of total mobility trips in Dubai into self-driving trips by year 2030,” he added.

The third edition of the challenge comes with a new competitive theme focused on self-driving buses. It reflects RTA’s commitment of moving forward with the Dubai Government’s pioneering efforts to expand the use of self-driving transport technology at all levels. A fund of $2,000,000 is allocated for industry leaders and $300,000 is allocated for local academia.

All local and international companies listed under the category of ‘Industry Leaders’ can take part in the challenge. The company has to be registered in the country of origin or locally, and has to operate in developing or operating self-driving buses. The ‘Local Academia’ category covers all universities, institutes and research centres in the UAE interested in autonomous technology research and engaged in developing relevant models.