BPO in the Self-driving Vehicle Technology: Data Management and Testing

Self-driving vehicles seem like magic. The idea of cars running without steering wheels sounds like it leaped straight from a fictional book. Rather than hocus-pocus, however, creating autonomous vehicles requires a lot of manual labor.

Companies working on this technology hire numerous employees and may even leverage business process outsourcing (BPO) workers, who train artificial intelligence (AI) to recognize trees, pedestrians, and other obstacles. Dedicated third-party teams achieve this by annotating each frame of video footage recorded from prototype cars driving around testbeds.

This article discusses the role of BPO in developing, testing, and improving self-driving vehicles.

BPO’s Role in the Data Management and Testing of Self-driving Vehicles

Considerable advances in computer power, sensor quality, and AI laid the technological foundations of the driverless revolution. The first self-driving car was created in 2015. Autonomous driving is expected to generate $300 billion–$400 billion in revenue by 2035.

Despite these advances, humans are still needed behind the scenes to draw boxes around cyclists and highlight road signs to power autonomous cars. These manual labor roles open up BPO opportunities for annotation services.

The same process applies to any Al system. Computers learn via human workers who feed them vast amounts of manually labeled data. They then use models and algorithms to understand patterns and objects when they see them.

BPO plays a crucial role in the data management and testing of self-driving vehicles. These vehicles rely heavily on data to operate efficiently and safely. Managing and testing this data is vital to developing and deploying autonomous vehicles. Here is how BPO firms can contribute to these processes:

• Data collection and annotation. BPO providers assist in collecting and annotating large amounts of data, such as sensor data (radar, camera, LiDAR), GPS data, and vehicle telemetry data. Self-driving vehicles use the information to train their machine learning (ML) models. BPO firms employ annotators to label and tag data.
• Data quality assurance. Ensuring the quality of the data used for testing and training autonomous vehicles is critical. BPO companies can implement rigorous quality control processes to identify and rectify human errors or inconsistencies in information, which helps improve the reliability and safety of self-driving systems.
• Simulation and testing. BPO firms develop and maintain simulation environments replicating real-world driving scenarios to test self-driving vehicles. They let companies test autonomous systems in a controlled and safe setting. BPO teams manage the simulations, monitor results, and report issues, expediting testing cycles.
• Data storage and management. Self-driving vehicles generate large amounts of data, and managing and storing this data can be a significant challenge. BPO providers offer cloud-based data storage, archiving, and database management solutions to ensure information is accessible, secure, and well-organized.
• Data processing and analysis. BPO firms assist in data analysis and processing tasks, such as cleaning, transformation, and feature extraction. They can also perform data analytics to identify patterns, anomalies, and performance metrics related to the self-driving system’s behavior.
• Software testing and validation. The AI for self-driving vehicles needs rigorous testing to ensure safety and reliability. BPO providers can handle various software testing activities, including functional testing, regression testing, and integration testing. They also help in validation processes to ensure compliance with safety regulations.
• Compliance and documentation. BPO firms help manage the documentation required for regulatory compliance and safety certification. This includes maintaining records of testing and validation results, incident reports, and other essential documentation for government agencies and industry bodies.
• Ongoing improvement. BPO partners provide ongoing support for data management and testing processes, allowing companies to continually improve their self-driving systems. They can analyze test results, identify areas for enhancement, and assist in implementing changes and updates.

The human workforce is crucial in developing self-driving vehicles because it deals with complex data labeling and edge cases. The autonomous driving industry relies heavily on manual labor, and this is where outsourcing can help.

Service providers offer computer vision tasks and data annotation tools when they get their clients’ driving data. The four main tasks their systems perform are:

• Sensor fusion annotation
• LiDAR point cloud annotation
• Video and image annotation
• Semantic segmentation

Most data annotation vendors provide application programming interfaces (APIs) through which clients enter raw data. This feature allows the service providers to execute annotation activities with their tools.

Reasons to Outsource Data Annotation, Management, and Testing

Now that you know what BPO is in the self-driving vehicle industry, you might wonder whether you should build an in-house team or outsource.

According to Grand View Research, the worldwide data annotation and labeling market will exhibit a compound annual growth rate (CAGR) of 30.3% from 2021 to 2028, significantly boosting its 2020 value of $1.6 billion. This growth is partly due to the increasing adoption of AI and ML tools across industries, including autonomous driving.

The reasons to leverage BPO in the data annotation and testing of self-driving vehicles are as follows:

• High-quality services. Costs are a significant consideration when outsourcing data annotation services. BPO firms offer high-quality data annotation solutions at flexible prices.
• Modern technology and infrastructure. Data annotation vendors are sophisticated and up-to-date with cutting-edge AI, ML, and robotics technologies. Clients get the most advanced software tools and bespoke data annotation services.

When choosing an outsourcing partner, companies must ensure flexible engagements in the labeling loop. They must work with labelers who respond quickly and make changes in workflow based on the validation and model testing phase.

Specifics of outsourcing arrangements may vary widely. Some manufacturers may keep certain aspects of their self-driving technology development in-house while outsourcing other functions.

Data annotation and management are vital components of the development of self-driving systems. Outsourcing can help car companies analyze the large amounts of data needed to train and test autonomous vehicle algorithms.

The Bottom Line

Providing ground truth data annotation for autonomous driving systems is tedious, but it is critical to the project’s overall success. Get the best results by validating and training your algorithms with error-free annotations from reputable BPO providers.

As the autonomous vehicle industry evolves, many manufacturers explore different outsourcing strategies to leverage expertise and resources, reduce costs, and accelerate the development of self-driving technology.

Let’s connect to learn more about how BPO streamlines the production of self-driving vehicles.