Are AI and data labeling the missing pieces to boost connected & autonomous driving?
Evaluating and ensuring the quality of data labeling can influence technological advancements
Numerous innovative technologies shaping the future, like Connected Driving, Autonomous Driving and Intelligent Transport Systems (ITS), heavily depend on continuous data collection from various sources, like vehicles and road infrastructure. They collect information about drivers' behaviours, pedestrians in the surroundings, the weather and traffic conditions, vehicle performance and all kinds of additional details that can bring value to the connected road ecosystem to make it work. However, simply collecting data isn't sufficient for these future mobility technologies to function effectively: precise interpretation is essential. Here is where data labeling becomes crucial, playing a key role in ensuring the accuracy and reliability of the information, particularly critical when human lives are at stake.
Data labeling and its connection with AI and Future Mobility
Most people have already experienced the awe-inspiring capabilities of Artificial Intelligence (AI), enriching and facilitating our daily lives. But beneath AI's wonders lies a key element that allows it to happen: data labeling. Essentially, data labeling is the process of tagging data — like images, videos, or texts — with meaningful labels that provide context. These tags are used by AI algorithms to understand and interpret information accurately. Therefore, data labelling acts as the base of AI's learning journey, enabling it to comprehend and categorize data effectively.
While AI models can be trained using methodologies beyond exclusively relying on data labeling, in certain scenarios, data labeling remains indispensable for evaluating model performance and fine-tuning results to meet specific requirements. For this reason, data labeling stands as a crucial aspect, not only for AI development, but also for many technologies that use data collection to nurture their operation. It is the case of future mobility, where technologies fueling Connected & Autonomous driving and ITS, need to label the massive amount of data they collect to be able to use it.
This video shows an example of how data labeling works in a future mobility environment, where vehicles, traffic elements and pedestrians are constantly being identified. This detection is possible thanks to data labeling processes that interpret the data and tag the elements to differentiate them. This information is accessible and shared with the whole ecosystem to connect the road elements and boost traffic efficiency and safety. Nevertheless, the success of this process it's not just about labeling data, but guaranteeing quality in the data labelling process.
High-quality data can maximize technologies development
Evaluating data labeling quality is crucial to verifying that AI systems can handle diverse inputs and scenarios with consistency and trustworthiness. High-quality labeling allows AI models to be trained on reliable and representative data, leading to more accurate predictions and classifications across different scenarios and contexts. But how can quality be ensured in data labeling?
Different strategies can be implemented directly within the labeling workflow to guarantee a correct and precise data labeling process. For example, by establishing clear guidelines, standardizing labeling formats, providing comprehensive training to labelers, incorporating quality assurance procedures and error correction mechanisms, and offering real-time feedback to update guidelines and procedures, among many others.
However, one of the most important practices to check the quality is to conduct regular audits and evaluations of data labelling processes by a third party, to identify vulnerabilities and confirm their adequate operation, as well as to make sure that their deployment meets the regulatory demands in the different markets. Some companies are already evaluating their data labeling processes to strengthen the reliability of their services and processes, and elevate and consolidate their customers' trust.
Assessing the data labeling processes of LTS Group
DEKRA has supported LTS Group, a prominent player in the AI and ADAS systems field, in certifying the quality of the data labeling processes of its services. DEKRA, based on their longstanding testing & certification expertise, performed an assessment according to the ISO/IEC 5259-4, a standard that provides guidelines and frameworks for evaluating data quality in machine learning and analytics contexts.
During the certification, DEKRA assessed LTS Group's methods and processes for image, video and text annotations, as well as 3D sensor data. For that, DEKRA experts used the DEKRA AI Audit tool, a unique method DEKRA has designed specifically for AI technologies. LTS Group successfully met the requirements and specifications defined in the standard, so DEKRA has certified the quality of its data labeling processes and has awarded them with a DEKRA certificate that proves its compliance and demonstrates LTS Group's interest in ensuring the quality and trustworthiness of its services.
The importance of the symbiotic relationship between AI and Data Labeling
There is a crucial bond between AI and data labeling that makes its relationship symbiotic and fundamental. Data labeling provides the necessary information for training AI models, while AI technologies, in turn, optimize and enhance the data labeling process thanks to what they learn from that labeled data. This unique synergy proves the essential role both have in building the foundations and driving force for the advancement of AI technologies, and, consequently, to other technologies.
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