Unmanned Transport (autonomous transport, self-driving transport, driverless transport, or robotic transport) is a type of transport capable of moving without human intervention.

Unmanned Transport Vehicle - a highly or fully automated vehicle that operates without human intervention (Decree of the Government of the Russian Federation dated March 25, 2020, No. 724-r).

Unmanned transport uses various sensors to perceive the surrounding environment, such as thermographic cameras, radar, LiDAR, sonar, GPS, odometers, and inertial measurement units.

Control systems interpret sensor information to create a three-dimensional model (3D) of the surrounding environment. Based on this model, the vehicle determines appropriate navigation paths and strategies for managing road traffic (stop signs, intersections, main roads, speed, flow, etc.) and obstacles. High definition (HD maps) maps road maps are also an integral part of this technology.

IoT sensors, artificial intelligence, machine learning technologies, and big data analysis are all fundamental to the connected car and autonomous driving phenomenon. It is expected that by 2025, the global IoT automotive market will reach $541.73 billion, with an annual growth rate of 16.4%, and connected car shipments, according to Business Insider forecasts, will reach 65 million by 2030.

Unmanned transport is primarily needed to save on the logistics component of cargo delivery on long-distance routes. This savings amounts to hundreds of billions of US dollars worldwide. Secondly, the absence of the human factor eliminates cargo manipulation and reduces criminal and corrupt practices. Thirdly, the growing trend of replacing hydrocarbon fuels with electric ones will lead to simple, safer, and more digital solutions for self-driving transport.

Goal: To create a system for the support and facilitation of unmanned vehicle movement on the road network with a high level of safety.

Objectives: The preparatory geospatial work includes:

• 3D modeling of urban road conditions or complex surrounding environments, digital elevation models.
• Digitization of road traffic map objects in urban conditions:

1. Digitization of lane map objects.
2. Data digitization scale is 1:200.
3. Digitization of roadway polygons.
4. Boundaries of roadway polygons.
5. Movements trajectories of unmanned vehicles.
6. Centerlines of lanes should be separated into individual objects in the following cases:

• Change of lane markings on the right or left side of the lane.
• Merging with another lane.
• Lane division.
• Change in lane width.
• Change in vehicle speed on the lane.
• Entry to an intersection.
• Exit from an intersection.
• Entry to a pedestrian crossing.
• Exit from a pedestrian crossing.
• At a stop line.
• Transition from a straight road segment to a curved one.
• Entry to an artificial roughness.
• Exit from an artificial roughness.
• Change in stopping/parking possibilities in the lane.

7. Outline of maneuvers at intersections.

The extensive coverage on highways, high detail in cities, and surrounding environment that hinders the building and control of autonomous driving, the transition from hidden structures (tunnels, mountains) to open terrain, poor weather conditions on the roads at a given moment - all these can be solved with field geodetic and textural work combined with Earth observation data carriers from satellites, aerial platforms (UAVs), to mobile Earth observation laboratories:

• High positional accuracy (sub-meter level accuracy) in time (synchronized for the entire system to milliseconds), ensured by recordings from various position and control sensors.
• UAVs are capable of creating complex digital road high definition maps (hd maps) and road conditions, including digital elevation models.
• Precise geospatial descriptions of movements serve as an indisputable legal basis for resolving legal disputes and identifying insurance cases.