Localization Goes Independent
A new localization mode developed by the Russian company EvoCargo enables autonomous vehicles to operate safely even in environments where traditional lidar‑based systems fail.
Localization Without Crutches
Localization — the ability of an autonomous vehicle to determine its position in the environment — has long relied on a combination of lidar and pre‑built HD maps. Lidar scans the surroundings ten times per second and compares the result to a digital map, much like a human orients themself by familiar landmarks.
However, this approach has a critical weakness: in open fields, forested areas, tunnels, or near repetitive fencing, lidar lacks distinct reference points. The environment becomes too uniform, and the system cannot reliably match observations with the stored map.
EvoCargo, a Moscow‑based developer of autonomous freight platforms, has introduced a fundamentally different solution. Its new localization mode allows a driverless truck to continue moving safely using only GNSS satellite data (whenever available) and auxiliary sensors such as accelerometers, gyroscopes, wheel‑path counters, and speed meters. The system no longer relies on constant lidar support and instead computes its spatial position through inertial and satellite inputs.
Pathways for Expansion
EvoCargo is among the few organizations that have not only designed but industrialized such a solution. Its significance extends far beyond another software upgrade. The technology removes a fundamental bottleneck: using autonomous trucks in areas with inadequate infrastructure.
The new localization mode opens deployment pathways across manufacturing sites, forest operations, agricultural facilities, and remote logistics terminals — locations where lidar‑based systems consistently fail.
The technology also carries notable export potential. If it proves scalable in regions with harsh climates and poor infrastructure, it could find strong demand in the CIS, the Middle East, Africa, and Asia — markets where logistics environments mirror many of Russia’s own challenges.
How the Technology Emerged
The journey from lab prototype to commercial fleet deployment is notoriously complex. EvoCargo entered the market in the early 2020s, with its first autonomous electric vans — the Evocargo N1 — deployed in retail logistics hubs such as the chain Gazpromneft-Snabzhenie (warehouse complex). These were among Russia’s earliest real‑world autonomous freight pilots.
In parallel, the company supported discussions around “smart road” concepts, including potential use of the CKAD highway as an autonomous testing corridor. Its deployments expanded from warehouse environments to industrial plants, including operations at EVRAZ NMTK.
Globally, many ambitious autonomous‑trucking startups — such as Argo AI and Starsky Robotics — failed to reach commercial scale despite significant funding. Against this backdrop, EvoCargo’s steady progress stands out.
Forecasts and Strategic Outlook
EvoCargo’s solution represents a meaningful technological breakthrough: autonomous localization without constant lidar support fundamentally broadens where driverless trucks can operate. This improves economic feasibility in environments previously considered unsuitable.
Over the next two to three years, EvoCargo’s autonomous trucks are expected to be deployed much more broadly — at manufacturing sites, distribution centers, and logistics hubs. The company’s autonomous vehicles will also gain the ability to operate on public roads, not just within warehouse facilities.
If current trends hold, the technology could become a competitive export product for emerging markets in the CIS, Latin America, Asia, and Africa.
Further development is expected to push toward even greater independence from GPS — relying more heavily on advanced inertial systems and visual analysis. Integration with smart‑road infrastructure and adoption of electric and hydrogen platforms will further reduce environmental impact.
EvoCargo’s project represents a pivotal point at which autonomous logistics in Russia begins transitioning from an experimental phase to full industrial scaling.
