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Introduction
The automotive industry is rapidly embracing technologies for autonomous driving, such as systems based on Artificial Intelligence (AI), Machine Learning (ML) and Computer Vision systems. For training and validation of these systems, a high level of accuracy in annotated datasets collected from cameras, LiDAR sensors, radar systems and other perception technologies are essential to automakers. German and Japanese auto manufacturers' major automotive technology vendor came to Infosearch BPO for help in meeting their sizeable point cloud and autonomous vehicle annotation demands.
Infosearch BPO supported the client with special annotation services to create reliable perception models for autonomous driving applications and guaranteed high-quality training data for object detection, classification, tracking and understanding of the environment.
The background
The client is a global automotive technology solutions provider with top-level automobile manufacturers in Germany and Japan, engaged in the development of Advanced Driver Assistance Systems (ADAS) and autonomous vehicle technologies. The company develops AI-powered perception systems for vehicles that help them detect and react to road conditions, traffic participants and the surrounding environment.
With the growth of autonomous vehicle initiatives, the client needed an annotation partner that is capable of handling the massive amount of data generated by LiDAR point cloud, camera images, and sensor fusion while maintaining strict quality and accuracy standards.
Objectives
The main aims of the project were:
Process
Infosearch BPO has designed a proper workflow to guarantee accuracy, consistency, and promptness.
Requirement Analysis
The project team worked with the client to define the formats of the data, the specifications for annotating the data, the classes of objects, the quality requirements, and the schedules for delivery. Specific guidelines were drawn up to guarantee consistency in the annotations.
Team Selection and Training
A dedicated team of quality analysts and annotators was established for the project. The team members were given training in autonomous driving concepts, interpretation of the point cloud obtained from a LiDAR sensor, and the standards of object classification as well as the annotation procedures applicable to the client's specific situation.
Point Cloud Annotation
The following activities were used as annotations:
Quality Assurance
A multi-stage quality control process was carried out:
Data Delivery
Annotated dataset delivered according to agreed schedule – securely. The client received regular updates on the project's status and quality measures during the engagement.
The ability to interpret complex LiDAR data.
The point cloud datasets were highly dense and unstructured, with only specialized knowledge and expertise to correctly identify and classify objects in a 3D space.
Diverse Driving Environments
The datasets consisted of urban roads, highways, tunnels, parking areas and different weather conditions in Germany and Japan, which made the annotation even more complex.
High Precision Requirements
High consistency of annotations is crucial for autonomous driving systems, because any slight differences in the consistency of the annotations can affect the performance of the model and the safety of the vehicle.
Large Data Volumes
The challenge of the project was efficiently processing millions of LiDAR points and thousands of sensor recordings, which demanded efficient workflow management and scalability of resources.
Dynamic objects and occlusions
There were often many overlaps and partial occlusion between vehicles, pedestrians and bicycles, which made object tracking and segmentation difficult.
Results
The project was very useful for the client and automotive partners:
Overall, the project has helped the client continue to innovate and develop new mobility solutions that are safer and more reliable, advancing their autonomous driving vision.
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