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Mohsen Ahmad, CEO of the Logistics District — Dubai South, and Andrey Bolshakov, Founding CEO, Evocargo, stand next to the driverless truck after the MoU signing ceremony Image Credit: DMO

Dubai: In line with Dubai’s drive to become the world’s smart mobility hub, Dubai South today signed a memorandum of understanding (MoU) with Evocargo for the launch of the UAE’s first autonomous vehicle trials for cargo at the master development’s Logistics District.

The trials will see EVO. 1, Evocargo’s unmanned electric logistics vehicle, navigate Dubai South’s Logistics District from December until February 2023. A key objective of the trials is to enable Evocargo to modify and redesign EVO. 1 specifically for the MENA region. During the trial period, a remote operator will be stationed on-site in the Control Centre to manage the platform. The centre, located in Dubai South’s Logistics District, includes a software suite to monitor the EVO. 1’s operation, check the serviceability of the sensors, and identify any errors, according Dubai Media Office press release.

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Mohsen Ahmad, CEO of the Logistics District — Dubai South, and Andrey Bolshakov, Founding CEO, Evocargo, attended the MoU signing ceremony. Dubai South is the emirate’s largest single-urban master development focusing on aviation, logistics and real estate. The Dubai-headquartered Evocargo is a logistics service provider that develops and provides electric autonomous transportation platforms.

Dubai is a global logistics hub with an unrivalled freight capacity and connectivity. Perfecting electric and driverless truck technology for the region will help it further reduce its carbon footprint and reinforce the city’s green credentials.

Self-driving private vehicles

Unlike other cities’ and countries’ initiatives that focus solely on enabling self-driving private vehicles, Dubai’s Self-Driving Transport Strategy is multimodal and encompasses targeting all seven modes of the public transport fleet, including metro, tram, bus, taxi, marine transport, cable cars and shuttle. When fully implemented, the strategy will help reduce transportation costs by 44% or Dh900 million, saving Dh1.5 billion through the reduction of environmental pollution and Dh18 billion through raising the efficiency of the transport sector by 20%.

Mohsen Ahmad said: “We are delighted to enter into a strategic agreement with Evocargo to launch the UAE’s first autonomous trials, setting new global benchmarks and consolidating the leadership status of the country’s logistics sector. Besides improving operational efficiency, our partnership will help scale supply chain operations and achieve sustainability. At the Logistics District, we are mandated to support the industry and accelerate the UAE’s rapidly advancing logistics sector.”

Andrey Bolshakov said: “This is Evocargo’s first venture into autonomous vehicles in such a global multimodal logistics platform. This trial is a significant milestone for the company as it unlocks opportunities to expand our products in the strategic Middle Eastern and Asian markets.”

Effective, electric, eco-friendly

The lifting capacity of the driverless platform is 2 tonnes, and it can accommodate up to six EUR-pallets moving at 25km/h for up to 200km. Charging a vehicle for a full day’s operation takes 40 minutes to six hours, depending on the outlet.

The security system of the EVO. 1 platform has four tiers: the computer vision of the space around the vehicle, an automatic diagnostic system, a remote-stop system, and a standby pneumatic braking system.

Effective fleet management of EVO. 1’s automatic pilot systems increases the efficiency of freight transportation while significantly reducing truck downtime. Robotisation and using electricity and hydrogen fuel cells instead of conventional fuel offer cost efficiencies.

Evocargo has 37 protected inventions and technologies. Evocargo’s patents cover algorithms for visual positioning, automatic mapping, the calibration and integration of sensors and cameras, methods for selecting safety speeds, and the parameters for dynamic models.