Nexter AI

Autonomous Driving Data Annotation, NEXTER AI Leads the Way

NEXTER AI

NEXTER AI is a specialized data processing company

that supports the development of

autonomous driving technology.

We provide the best annotation solutions.

Nexter AI

NEXTER AI hopes to establish a long-term partnership based on trust and mutual benefit, leveraging our experience in building autonomous driving training data.

Performance Improvement

Accurate and consistent annotation provides optimal data for AI model training, enhancing the model's prediction accuracy.

Cost Reduction

Operating a global team allows for flexible resource allocation according to project needs.

Specific workforces can be quickly reassigned as needed to adjust work speed, thereby managing costs efficiently.

Schedule Reduction

Through efficient processes for rapid data processing and review, we provide data at the required time, accelerating the overall project progress.

LD & RB

LANE DETECTION & ROUND BOUNDARY

Recognition of lanes and lane-obstructing obstacles during autonomous driving Lanes (solid lines, dashed lines, guide lines, etc.)

Obstacles (walls, temporary barriers, fixed barriers, etc.)

NEXTER AI has sales channels in the US, China, Japan, and Europe.

Global sales channels

Challenges in Building Dataset for Autonomous Driving

Securing a Pool of Skilled Labelers

As the complexity of labeling increases with the advancement of autonomous driving systems, systematically training skilled workers to build

high-quality data becomes increasingly important.

Project Flexibility

To enhance project efficiency, it is crucial to have internal experts who can flexibly respond to changes in development schedules, data demands, and policy shifts. Effectively training workers in response to these changes is also important for maintaining project stability.

Challenges in Building Dataset for Autonomous Driving

Data Bias

Due to the varying driving environments in different regions, it is essential to train on diverse driving environments and scenarios. To ensure the global applicability of autonomous driving systems, it is important to secure diverse data that is not biased toward specific regions.

Data Inconsistency

Inconsistent data can lead to performance degradation and errors in the model. Therefore, continuous training and a rigorous review process are necessary to ensure that workers adhere to consistent standards and produce reliable data.

Projects

Based on our experience in various fields,

we effectively handle high-complexity data processing.

1 3D LiDAR

3D LiDAR sensor data processing

2 LD&RB

Lane detection and road information data processing

3 LSD

Lane detection data processing

4 TSR

Traffic sign recognition data processing

5 LSR

Driving vehicle status recognition data processing

6

Scene classfication

Dynamic video-based road driving environment classification

7

FSD

Drivable space classification, terrain and boundary information data processing

Using data collected from various road environments through LiDAR sensors, we annotate objects such as roads, vehicles, pedestrians, traffic signals, and signs. This process involves identifying each object's location, size, attributes, status, occlusion, and orientation.

3D Lidar

OD

OBJECT DETECTION

Detection of various dynamic objects in the driving environment, including animals, buses, cars, pedestrians, bicycles, and motorcyclists.

SOD

STATIC OBJECT DETECTION

Detection of static objects related to parking and obstacles, such as fixed obstacles, parking-related objects, parking locks, parking stoppers, and barrier bars.

TSTLD

TRAFIC SIGNS & TRAFIC LIGHTS DETECTION

Detection of signal system-related objects, including traffic signs and signals, the interiors and exteriors of traffic signs, and signals for both vehicles and pedestrians.

Labeling of objects

only in the front view of the vehicle

Labeling of objects

in the 360-degree surrounding view of the vehicle

*With the advancement of autonomous driving technology

The same object viewed from different angles

This requires labelers to have high perceptual skills to distinguish the same object from different angles.

NEXTER AI

NEXTER AI has a skilled pool of experts to meet these advanced 3D labeling requirements.

OTHER
PROJECTS

LSD

LANE DETECTION DATA PROCESSING

Detection of traffic signs and vehicle types in low-visibility and poorly-lit night conditions

TSR

TRAFIC SIGN & RECOGNITION

Object recognition and annotation of road signs related to driving during vehicle operation.

Classification of main signs and auxiliary signs.

Classification of road signs by country (Europe, USA, China, Japan, Korea, etc.)

LSR

TRAFIC SIGN & RECOGNITION

Recognition of vehicle lights. Annotate the front and rear positions and types of objects based on the detected information.

SC

SCENE CLASIFICATION

Classification of weather, time of day, and road conditions (e.g., highway, rural, general roads) through video into 8 basic scene types, further detailed into 25 classes.

FSD

3D Perception Multi Vision FREE SPACE DETECTION

Segmentation of navigable and parking spaces (free space) for the recognition of road surfaces and planes, boundaries, obstacles, etc.

Annotation of curbs, vehicles, artificial structures, parking stoppers, general objects, terrain, etc.

CO-WORKING PROCESS

Nexter AI

NEXTER AI hopes to establish a long-term partnership based on trust and mutual benefit, leveraging our experience in building autonomous driving training data.