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The rapid development of autonomous vehicle technologies is driving a growing need for large volumes of high-quality data to train, test, and validate computer vision algorithms and environmental perception systems. Modern driverless cars use a suite of sensors, including cameras, LiDARs (LiDAR), radars, and other sensors, which provide information

Autonomous cars in traditional configurations face physical limitations: blind spots around corners, pedestrians blocked by oversized trucks, fog, or the limited range of their own LiDAR sensors. The concept of cooperative perception, enabled by V2X (Vehicle-to-Everything) technologies, helps the autopilot see the world through the eyes of other

3D occupancy grid annotation divides a 3D scene into cubic voxels (volumetric pixels) and classifies each of them as occupied, free, or unknown. This approach is used for training unmanned vehicles because it allows modeling arbitrary geometric shapes and objects that are difficult to describe with standard 3D bounding boxes.

Creating a reliable reference dataset (cross-sensor ground truth) is a challenge for autonomous vehicle (AV) developers. Each sensor sees the world in its own way: cameras provide semantics but are weak at determining distance; LiDAR provides 3D geometry but cannot see through heavy rain or fog; Radar measures speed

The rapid development of AI, computer vision, and machine learning has had a significant impact on the automotive industry. Modern driver assistance systems (ADAS) and autonomous vehicles use algorithms that require large amounts of well-labeled data for training and evaluation. Traditionally, image and video annotation has been performed manually.

Currently, automotive autonomous and semi-autonomous driving systems rely on a complex suite of sensors that includes optical cameras, radars, and high-precision LiDARs. Despite their significant operating range and ability to build detailed three-dimensional maps of space, these technologies prove vulnerable in the immediate vicinity of the vehicle&

LiDAR has become an important perception sensor in autonomous vehicles, robotics, industrial automation, and smart infrastructure. By creating highly accurate 3D point clouds, LiDAR enables AI systems to detect obstacles, estimate distances, localize objects, and generate detailed environmental models. However, environmental conditions, aging equipment, contamination, and mechanical failures can degrade

Camera calibration is one of the fundamental stages in building modern computer vision systems, as it ensures an accurate geometric correspondence between the scene's three-dimensional space and its two-dimensional image. Determining the camera's internal and external parameters, as well as compensating for optical distortions,

The development of modern advanced driver-assistance systems (ADAS) and autonomous transport directly depends on the vehicle's ability to continuously and flawlessly recognize its surrounding environment. Currently, most autopilot architectures rely on a synergy of optical cameras and LiDAR, which provide high resolution and precise geometry under ideal

Driver assistance systems (ADAS) combine information from cameras, lidar, radar, GPS, inertial module, and other sensors to create a comprehensive view of the road. This process, known as sensor fusion, improves the accuracy of perception, object detection, and decision-making in complex environments. However, effective sensor fusion requires precisely synchronized

The rapid development of industrial automation and the widespread adoption of collaborative robots (cobots) are significantly transforming modern manufacturing processes. This opens up new opportunities to increase productivity, production flexibility, and operational efficiency. In modern robotic solutions, data annotation is becoming increasingly important, especially for systems that use computer vision,

Modern automation of logistical and industrial facilities requires a radical change in artificial intelligence architecture: a transition from egocentric behavioral models to collective interaction systems. Traditional training datasets instill a purely individual logic of actions in a robot, where the machine relies exclusively on its own sensors, independently calculates its

Robotics can manipulate rigid objects, but deformed objects remain a challenge in embodied AI. Objects such as clothing, cables, ropes, bags, and soft materials change shape during interaction. Their behavior depends on gravity, contact forces, friction, and previous states, making perception and manipulation more complex. Robot foundation models require specialized