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In the dynamic world of automotive technology, the integration of advanced driver assistance systems (ADAS) and artificial intelligence (AI) are revolutionizing vehicle safety, efficiency and autonomy. Using a network of cameras, radar and sensors, these systems create a real-time picture of the surroundings, enabling vehicles to react swiftly to potential dangers.

Core ADAS features such as automatic emergency braking (AEB), adaptive cruise control (ACC), lane departure warning (LDW) and blind spot detection (BSD) are becoming a requirement in the modern production of vehicles. Given their impact on the safety of humans, it's crucial that every manufacturer prioritizes the reliability and regulatory compliance of these systems. 

This is done through a robust field operational testing (FOT) program, with training data specifically created for ADAS model testing and validation. Since this process involves technical and operational investments that are outside of the strategic business focus of most manufacturers, many choose to partner with external solution providers capable of managing end-to-end FOT projects. This includes everything from sourcing vehicles and qualifying drivers, to planning routes and calibrating sensors, as well as specialized technical support for collecting, labeling and validating the data.

An FOT data collection program is a massive undertaking with many integral components. Read on to explore the processes involved in such a program, the importance of sourcing high-quality data and some key points to consider when selecting an optimal FOT data solution to enhance your ADAS.

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Beyond the drive: Considerations for top-tier FOT data collection programs

Embarking on a comprehensive FOT data collection program for ADAS and autonomous vehicles includes a number of considerations. Some of these include sourcing different vehicle types, fitting those vehicles with sensors, planning optimal routes with specified conditions and hiring experienced engineers, drivers and technical staff.

Beyond these practical considerations are some key criteria that often go overlooked when commencing this type of project. These include data bias concerns, data privacy considerations, the complexities involved in storing and managing such massive amounts of data and the expertise and tools needed to efficiently label the data.

Let's look at these four critical components in detail.

One of the biggest challenges in FOT data collection lies in ensuring the data collected accurately reflects real-world driving scenarios. Insufficient or biased data can lead to misinterpretations of cause and effect. As a result, your advanced driver assistance systems might struggle to adapt and perform safely in unforeseen driving situations.

One of the critical ways to combat bias in ADAS is to ensure that FOT datasets encompass diverse driving conditions, including various weather, lighting and road surfaces. For example, one of our client's projects required leveraging our expertise to hire multiple drivers, one acting as a driver and one as a co-driver, to drive in turns during both day and night, in various cars. Using our proprietary software, we optimized route planning to cover a wide range of cities and scenarios of interest in more than 30 countries — including Taiwan, Japan, the United States, South Korea and several countries in the European Union (EU) — with daily data collection schedules. The end result was high-quality data that was representative of a wide breadth of real-world scenarios.

For driver monitoring systems (DMS) and occupant monitoring systems (OMS), FOT data collection also includes monitoring the vehicle's interior, driver and passengers, as well as collecting in-cabin audio data for car assistants. To mitigate dataset bias, it's crucial that the vehicle's occupants represent diverse demographics. For example, to develop a driver monitoring system that accounted for diversity, we crowdsourced 400 global participants from our AI Community who satisfied demographic variances such as skin tone, physicality, ethnicity, age and gender for driver data collection. This eliminated the need to ship the vehicle and recording systems out of the country and provided data to develop gesture recognition and gaze-detection models.

FOT data includes potentially sensitive information such as personally identifiable information (PII), as well as information about drivers' destinations, routes and locations near their homes. Such video data, GPS information and vehicle data can pose significant privacy risks. Further, the data can often be subject to regulatory compliance. For example, FOT data often includes video footage of other road users, including pedestrians, cyclists and passengers who haven't consented to being recorded. Under Article 4 of the EU's General Data Protection Regulation (GDPR), images showing faces and license plates are classified as personal data, making it critical to ensure collected data is well-protected and inaccessible to unauthorized parties.

Best practices for ensuring privacy protection and compliance with regulations such as the GDPR include implementing robust anonymization techniques, including:

Data masking, PII scrubbing and data aggregation to obscure personal identifiers.

Blurring faces and license plates in video footage to protect non-consenting individuals' identities.

Using end-to-end encryption for data storage and transmission and implementing strict access control mechanisms, including multi-factor authentication and access log audits.

Optimizing data collection by selectively gathering only essential data for ADAS functionality and storing it securely.

3. Meeting the computational and storage demands

Computational demands of real-world driving data collection present an operational challenge. Take for example the fact that a single vehicle equipped with six high-definition cameras and radar systems generates over 2 terabytes of data per hour. To effectively manage this massive and unpredictable influx of data, you need to implement storage systems like Azure data disks, external solid-state drives, AWS Snowball devices and cloud storage solutions. These storage systems can also scale efficiently with the increasing data volume and complexity.

Recently, we led a project mapping the city of Los Angeles using a vehicle moving at a speed below 20 miles per hour with minimal route repetition and a 50:50 ratio of night to day. To achieve this, we developed proprietary route planning software to avoid city route repetition and optimize the data collected to reduce computational overheads. Our enterprise-grade relationship with cloud storage providers helped us deliver one-week vehicle-to-cloud data transfer with a daily capture of 11 terabytes.

Further, a network of high-performance servers and storage is necessary to house the preprocessed and enriched data, which includes information on drivers, trips, situations encountered and performance indicators. Driving data collected in these projects usually amasses between 10-30 terabytes per day, running into hundreds of terabytes per week and reaching petabytes within a quarter. To adequately collect, store and transfer this data, the infrastructure requires careful orchestration, including the ability to schedule and manage all resources (e.g., storage, computing power and workforce) in a centralized location to maximize throughput and productivity. Additionally, regular technical maintenance tasks like data backups, software updates and hardware inspections are vital to prevent system failures and ensure smooth operation.

Recently, we collected roughly 125,000 miles of data across 30 countries, with a daily throughput of up to 7 terabytes per vehicle. Due to vast amounts of sensor data, video feeds and diagnostics generated by collection vehicles, the customer required robust data storage infrastructure and efficient data management techniques. To address this, we used Microsoft Azure to design a data storage and delivery solution. Leveraging Azure's capabilities, we implemented efficient data transfer and logging processes, ensuring seamless data management.

4. Transforming raw data into actionable insights

Ultimately, the raw data sourced during FOT data collection is just the beginning. This data needs preprocessing, which involves tasks like filtering, cleaning and enriching it with additional contextual data such as weather, traffic conditions and road classification.

The data labeling task for FOT involves a sophisticated and time-consuming process to extract and annotate relevant information about driving situations from the vast amount of data from various sensors (e.g., cameras, lidar and radar). It also requires a specialized workforce with the right skill set, including knowledge of driving rules and scenarios, attention to detail, good visual perception and the ability to follow complex guidelines. To increase the efficiency of this process, we leverage automated labeling from our industry-leading Ground Truth Studio (GTS) platform. With built-in project and user management tools, AI-enabled annotations and prelabeling and configurable workflows, GTS supports multiple data types such as image, video and 3D sensor fusion. Further, our data labeling work is carried out by thousands of annotators who continue to support ambitious labeling pipelines that drive global ADAS, autonomous vehicle and in-cabin technologies.

Leverage the benefits of working with a long-term ADAS data services partner

High-quality, vetted data is crucial to train and test your computer vision systems for navigating the complexities of the real world. Working with a partner that offers a comprehensive, fully-managed solution encompassing all of your automotive computer vision data collection and labeling needs can help. Such a partner can assist with everything from project management to scaling the workforce you need for collection and annotation to providing that workforce with efficient labeling tools.

Our global reach, supported by a robust partner network, enables us to conduct diverse and comprehensive data collection worldwide. To help reduce real-world testing costs and accelerate your ADAS development, we also offer a comprehensive library of curated and fully managed FOT datasets.

Leverage our team of AI and FOT specialists to design and execute robust automotive data collection programs that will help you build the future of safe and secure autonomous driving.

Contact our team of experts today to discuss your next ADAS enhancement project.

Discover the processes involved in field operations testing for enhancing advanced driver assistance systems and key considerations for program success.

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ADAS field operations testing: Four essential considerations for program success

On this episode, we ask big questions about how customer loyalty is formed — and how AI can be leveraged to build stronger connections with customers.

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What does customer loyalty look like in the age of AI?

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Explore the transformative potential of Contact Center-as-a-Service (CCaaS) solutions and learn how to navigate the evolving landscape of AI-driven customer interactions with this executive brief from Frost & Sullivan.

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The CCaaS breakthrough: Redefining customer interactions with AI

Join Ajinkya Malasane, senior director, product strategy and marketing for TELUS Digital (formerly TELUS International), and Vikram Chatterji, co-founder and CEO of Galileo, a generative AI (GenAI) evaluation company, as they provide efficient and effective ways to solve key GenAI project blockers for easier deployments.

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Crafting production-grade GenAI applications

Discover how TELUS Digital helped a world-leading communications and technology company streamline workflows and enhance its quality standards by leveraging a cloud-native development strategy and advanced automation solutions, including AI-powered test-driven development (TDD).

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Optimizing app development with cloud-native solutions and automation

Optimizing application development with cloud-native solutions and automation

Watch this recorded webinar from Everest Group and TELUS Digital (formerly TELUS International) for new insights to help you skillfully transform your digital customer experience.

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Webinar: How to supercharge your CX with AI-fueled digital transformation

How to supercharge your CX with AI-fueled digital transformation

Discover how TELUS Digital used our proven field operations testing (FOT) experience to create a high-quality dataset for training advanced driver assistance systems (ADAS) and autonomous vehicles (AV).

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Curating high-quality data for the training and validation of ADAS and AV models

On this episode, we explore how eco-friendly apparel brand Frank And Oak evolved its business model to adapt to customer expectations — and how data, feedback and technology are used to optimize its CX strategy.

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What is the Frank And Oak secret for adapting to changing customer preferences?

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Organizations have a responsibility to create and maintain digital experiences that are truthful, welcoming and safe. Learn how brands can keep pace with the growing levels of user-generated content (UGC) to build trust with their customers and protect their reputation.

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The future of content moderation: Strategies and tools for 2024 and beyond

Join industry experts from TELUS Digital and Everest Group as they explore how leading technologies, such as generative AI, automation, analytics and more, are delivering value for organizations without compromising service quality.

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Efficient CX delivery: A tech-driven approach

Discover how TELUS Digital assisted a leading U.S. autonomous vehicle (AV) developer in refining the scenario comprehension and motion planning capabilities of their AV bot.

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Enhancing an autonomous vehicle bot’s scenario understanding with tailored fine-tuning datasets

Discover how TELUS International successfully deployed a robotic process automation (RPA) solution for a leading waste management company to minimize time-consuming tasks and reduce the errors associated with manual data entry.

Implementing robotic process automation to streamline critical procurement processes

Discover how TELUS Digital successfully deployed a robotic process automation (RPA) solution for a leading waste management company to minimize time-consuming tasks and reduce the errors associated with manual data entry.

On this episode, we explore strategies for building a compelling business case for customer experience (CX) investment — and how to optimize the budget you’ve secured.

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How can leaders win and optimize investment in CX?

Forrester Budget Planning Guide 2025: Customer Experience Hero

To succeed, customer experience (CX) leaders must invest in strengthening core capabilities that enable their organizations to turn customer-centric strategies into real, impactful experiences. This guide from Forrester offers data-driven insights to help you determine where to increase, decrease or experiment with your CX budgets for 2025.

Forrester’s Budget Planning Guide 2025: Customer Experience

TELUS Digital recently conducted a survey that explored the customer service preferences of consumers today. Explore the results.

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2024 customer service preferences 

Infographic: 2024 customer service preferences

See the results our global, disruptive client achieved after they transitioned to Contact Center Outsourcing Solutions from TELUS Digital.

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Bring efficiency to your contact center

Bringing efficiency to a leading food delivery platform's overwhelmed contact center

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Discover how TELUS Digital safeguards AI training data quality through a comprehensive fraud prevention framework.

Discover how TELUS Digital safeguards AI training date quality through a comprehensive fraud prevention framework.

TELUS Digital Fraud prevention in crowdsourcing AI training data

Fraud prevention in crowdsourcing AI training data

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Learn how TELUS Digital’s team of experts successfully delivered data on real-world driving actions ranging from the mundane (bringing the vehicle to a stop) to high-risk (driving while distracted).

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Collecting in-field driving data to train a leading insurance provider’s rewards application

Accelerate your machine learning models

On this episode, we ask big questions to debunk prominent myths and misconceptions surrounding automation in customer experience.

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What do people get wrong about automation?

Architect your generative AI strategy for success in CX. Learn to identify manageable use cases, navigate build vs. buy decisions and leverage partnerships.

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A blueprint for GenAI use case identification in CX

Discover how our workforce management services can transform your enterprise and boost your results.

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Frost & Sullivan, a globally recognized market research and consulting firm, recently published an executive brief titled, The CCaaS breakthrough: Redefining customer interactions with AI. This comprehensive report identifies a number of emerging global trends, disruptions and new technologies shaping the contact center industry.

In this article, we delve into five major trends highlighted by Frost & Sullivan that are impacting contact centers. Read on to see how advancements in technology will reshape how contact centers enhance CX, drive agent productivity and equip their workforces with the right tools.

1. Rapid adoption of cloud-based technology platforms and architecture

One of the most significant contact center industry trends, according to Frost & Sullivan, is the shift toward cloud-based platforms. This refers to the use of internet-based platforms and infrastructure to deliver services traditionally handled by on-premise systems. Instead of relying on local servers and hardware, contact centers using cloud-based platforms can access a suite of tools and services remotely, hosted by third-party providers. These cloud platforms typically encompass everything from call routing and workforce management to customer relationship management (CRM) systems, making them an all-in-one solution for modern contact center needs.

According to Frost & Sullivan, seven key factors are driving rapid cloud adoption:

The end-of-life of existing system platforms: Many legacy contact center platforms are becoming outdated or are no longer supported by vendors. As these systems become less reliable and harder to maintain, businesses are increasingly turning to cloud-based solutions for contact center modernization. Today' cloud-based technology offers advanced capabilities and ongoing support without the need for costly replacements or upgrades.

The ability for premises-based organizations to add CCaaS in hybrid configurations: Businesses can combine the reliability of their current infrastructure with the flexibility and scalability of cloud-based services. This approach lets organizations gradually transition to the cloud without disrupting ongoing operations or requiring a full system overhaul.

New process automation and analytics capabilities: With AI-driven automation and real-time analytics, cloud contact centers can streamline workflows, enhance decision-making and improve customer interactions by predicting needs and optimizing agent performance.

Redesigned agent desktops: Having the ability to consolidate multiple tools and channels into a single, user-friendly interface enables agents to work more efficiently, offering quicker access to customer data and faster responses and ultimately improving the overall customer experience.

Digital transformation: Companies are looking to modernize their operations and deliver seamless customer experiences across digital channels. Cloud platforms are well-suited to support omnichannel services, allowing businesses to meet customer expectations in a digital-first world.

Richer integrations to third-party applications: Integration with applications such as CRM, marketing automation and ecommerce systems enables businesses to create a more connected and streamlined customer journey by pulling data from various sources to provide personalized and efficient service.

Advanced self-service applications: Applications like AI-powered chatbots allow customers to resolve simple inquiries without needing to speak to an agent. These tools reduce operational costs, enhance customer satisfaction and free up agents to handle more complex tasks.

One of the ways organizations are capitalizing on cloud technology is through the implementation of Contact Center as a Service (CCaaS) solutions. CCaaS is a cloud-based solution that delivers all essential contact center functions through a subscription model. This approach enables businesses to access advanced tools without the need for significant upfront investment in hardware or software. According to Fortune Business Insights, the CCaaS market is expected to grow from $6.08 billion in 2024 to $24.45 billion by 2032. This expected growth can likely be attributed to the scalability, flexibility and cost efficiency that these solutions offer organizations.

2. Constantly changing consumer behavior and preferences

Consumer behavior has undergone significant shifts, particularly in terms of how customers engage with contact centers. It's no surprise that changing consumer behavior and preferences made the list of key contact center trends to watch. As customers demand more personalized, seamless interactions, contact centers must continuously adapt to meet these evolving expectations.

For example, Salesforce's State of the Connected Customer report reveals that consumers interact with companies across an average of eight different channels. Additionally, 71% of consumers prefer using different channels — whether email, phone, in-person, online chat or others — depending on the situation. This underscores the need for businesses to offer an experience that allows customers to start an interaction on one platform and continue it on another without disruption.

Speed and responsiveness are also top-of-mind for the modern consumer. "We care about our time more than ever," said Jay Baer, keynote speaker, entrepreneur and author of The Time to Win: How to Exceed Your Customers' Need for Speed, during an episode of Questions for now, a TELUS Digital podcast that focused on building customer loyalty. Baer explained that brands that exceed expectations by responding to customer inquiries faster than anticipated can turn those customers into advocates. Baer's research also found that 50% of customers are less likely to spend money with a business if it responds slower than expected, and every year, these expectations for speed continue to rise.

In addition, research by Statista shows that 88% of customers now expect brands to have an online self-service portal. This reflects the growing preference for resolving simple issues independently, without speaking to an agent. As digital natives become the dominant consumer demographic, self-service options like online resources and automated solutions have become essential.

These are just a few examples of how shifting customer expectations will force brands to rethink their service strategies and adopt new technologies to stay ahead.

3. Increasing use of personalized automation using AI

Personalization has long been essential for brands looking to build lasting relationships with their customers, and this trend doesn't appear to be going anywhere anytime soon. A survey conducted by TELUS Digital revealed that over 60% of American consumers prefer a more personalized customer experience, even if it takes more time.

Recent advances in technology have significantly enhanced personalization capabilities. AI-driven automation, for instance, encompasses a wide range of applications, from chatbots to advanced routing systems and predictive analytics. Unlike traditional automation, which handles repetitive tasks using predetermined rules, AI-enabled solutions can analyze large volumes of data in real time. This enables dynamic, personalized interactions based on each customer's unique profile, providing more relevant and timely support. As a result, contact centers can offer a more tailored experience, reducing the need for human intervention in routine inquiries while improving overall customer satisfaction.

Bank of America's virtual financial assistant, "Erica," for example, has proven to be a game-changer in the financial services company's customer service since its launch in 2018. According to a release from Bank of America, "to date, Erica has responded to 800 million inquiries from over 42 million clients and provided personalized insights and guidance over 1.2 billion times." The assistant helps customers manage recurring subscriptions, monitor spending and handle inquiries like finding transactions or transferring money. Erica is especially effective at providing quick responses — 98% of clients receive answers within 44 seconds. And when further assistance is needed, Bank of America's Mobile Servicing Chat capability connects clients with a live representative who can answer more complex servicing questions.

4. Developing analytics and insights to drive business outcomes

Effective data analysis enables contact centers to monitor performance in real time, track customer behavior and identify patterns that can inform business strategies.

As Kory Lawszewski, vice president of global sales at TELUS Digital, pointed out in the report, 2024 CX Leaders Trends & Insights by Execs In The Know, sponsored by TELUS Digital, every customer interaction across various channels offers valuable insights that can be used to identify pain points and refine service strategies. "Review customer support call logs, interactions between your customers and your chatbots, replies on your social media posts, and anywhere else your customers interact with your brand," Lawszewski noted. By leveraging these insights, businesses can enhance their customer experience approach, anticipate future needs and make data-driven decisions to optimize service and efficiency. This feedback loop helps fine-tune processes, foster personalized interactions and ultimately drive higher customer satisfaction.

Analytics in contact centers typically fall into three main categories:

Descriptive: Focuses on analyzing historical data to understand what has happened, providing insights into past performance metrics like call volumes, customer satisfaction scores and average handling times. This helps contact centers identify trends and assess overall performance.

Predictive: Uses data models and machine learning to forecast future outcomes based on historical patterns. For example, it can predict call volume spikes, customer churn risks or the likelihood of resolving an issue during the first interaction.

Prescriptive: Recommends specific actions based on predictive insights. These analytics can be used to suggest how to allocate resources, optimize staffing levels or tailor customer service strategies to improve results.

AI-driven analytics are further changing the game by offering deeper, more actionable insights in real time. Traditionally, contact center analytics relied on historical data and basic metrics like call volume or average handling time. While useful, these insights were often reactive, providing a look at past performance without much predictive power. AI-driven analytics, however, change the game by enabling real-time analysis and predictive modeling.

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