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Press Releases

31 May 2022

RTA opens registration for 3rd Dubai World Challenge for Self-Driving Transport


(Over $2m prizes up for grabs, submissions will focus on self-driving buses)


an image from Dubai World Challenge for Self-Driving Transport

Under the patronage of His Highness Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum, Crown Prince of Dubai, Chairman of the Executive Council, the Roads and Transport Authority (RTA) is launching the 3rd Dubai World Challenge for Self-Driving Transport 2023, a first-of-its-kind event in the field of self-driving transport. Submissions to this edition of the challenge will focus on self-driving buses. 


RTA opened registration for the challenge as of Monday 30th May, 2022 through the designated website (www.sdcongress.com). Finalists will be announced in October this year, and winners will be revealed in October 2023 during the Dubai World Congress for Self-Driving Transport 2023. Candidates will compete for prizemoney amounting to US$2.3 million.


The challenge is part of RTA’s ongoing efforts to support the leading role of Dubai in self-driving transport, and its commitment to realising the Government’s vision to transform 25% of total mobility journeys in Dubai into driverless journeys by 2030. 


The 3rd edition of the congress is characterised by a new competition format in the field of self-driving buses. It highlights RTA’s commitment to driving the pioneering efforts of the Dubai Government and expanding the use of autonomous technologies across the board. $2 million have been allocated to Leading Companies, and $300,000 have been set aside for Local Academia. 


All local and global firms can make submissions under the Leading Companies category, provided that the company is registered in the country of origin or locally and operate in the field of developing or operating self-driving buses. The local academia category includes all universities, institutes and research centres in the UAE interested in autonomous technologies research and engaged in developing related models.
 

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