GAZelle Learns to Drive Itself
The electric GAZelle e-NN truck has completed real-world trials, covering 120 kilometers without a human behind the wheel. It is the first Russian project in which all core autonomous-driving algorithms were developed in-house.
A Test of Technological Independence
Specialists from the Moscow Institute of Physics and Technology, working together with engineers from the Moscow Automobile and Road Construction State Technical University, have successfully developed and tested an autonomous driving system for the electric GAZelle e-NN truck. On the grounds of the Alabuga special economic zone, the unmanned electric vehicle traveled 120 kilometers in fully autonomous mode, delivering a 625-kilogram load at an average speed of more than 17 kilometers per hour.
The technological core of the system is built around a tightly integrated set of hardware and software components. The vehicle continuously analyzes data from lidars, three-dimensional laser sensors that provide precise perception of the environment at distances of up to 100 meters, from multiple video cameras capturing scenes from different viewing angles, and from a GNSS+RTK satellite navigation system that delivers positioning accuracy down to the centimeter. All of these data streams are processed in real time on high-performance computing platforms embedded in the vehicle.
The algorithms that process this information were created by Russian researchers using proprietary neural network models. The system performs several critical functions simultaneously. It detects obstacles, moving objects, and stationary structures using lidar data, constructs a three-dimensional digital model of the surroundings in real time, localizes the vehicle in space, and calculates the optimal driving trajectory. If the satellite navigation signal is lost, a common occurrence in dense industrial zones, the system automatically switches to visual navigation based on stored landmarks and lidar inputs.
A Breakthrough for Closed Industrial Sites
The strategic decision to deploy autonomous trucks in closed industrial areas and production zones removes many of the constraints that arise when using self-driving vehicles on public roads. Within restricted territories, there is no need to undergo complex road-safety certification, interact with unpredictable traffic participants, or account for highly variable external conditions.
The economic potential of this approach is substantial. An autonomous truck can operate around the clock, running routes between warehouses, production facilities, and loading and unloading points with minimal human involvement. This translates into a reduction in operating costs of 30–50%, depending on usage intensity and route characteristics.
Future development plans include integration with other digital technologies, ranging from warehouse management systems and route optimization platforms to blockchain-based cargo tracking solutions and AI algorithms capable of forecasting logistics demand several days in advance. Together, this digital ecosystem could transform Russian industrial logistics into a globally competitive system.
A Track Record of Experiments
In 2020, the GAZ Group built its first autonomous GAZelle NEXT vehicle. That truck successfully completed pilot industrial operations at oil and gas fields in the Khanty-Mansi Autonomous Okrug, where it delivered cargo daily between production sites without a driver.
In 2021, autonomous GAZelle trials expanded to Gazprom Neft fields in Yugra. The control system, developed jointly with Nizhny Novgorod State Technical University, demonstrated the ability to navigate safely across different types of road surfaces, including stretches without lane markings, a common condition in northern regions of Russia.
In 2023, KamAZ conducted tests of autonomous heavy trucks at the East-Messoyakhskoye oil field on the Gydan Peninsula. These trials showed that major domestic truck manufacturers are actively integrating autonomous technologies into their production pipelines.
Russia’s focus on autonomous transport designed for complex industrial environments and cold climates creates a distinct competitive advantage.
From Independence to Commercial Reality
The project led by MIPT and MADI symbolizes a transition from experimental development to the early stages of commercialization in domestic autonomous transport. If progress continues at the current pace, the first industrial operators offering autonomous logistics services at large Russian enterprises could emerge as early as 2026–2027.
Over the next two to three years, testing is expected to expand to new sites, regional autonomous delivery operators may appear, and full certification of autonomous platforms under Russian and international safety standards is anticipated. In the medium term, domestic autonomous solutions are likely to integrate with Russia’s digital logistics ecosystems, giving rise to commercial operators providing services on a regular basis. Over the longer horizon, export potential also becomes realistic.
The GAZelle e-NN project demonstrates that Russia is not merely following global trends in autonomous transport but is developing its own independent approach to building self-driving systems.
