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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

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Navigating knowledge management: A complete guide for success

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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 sponsored by TELUS Digital (formerly TELUS International).

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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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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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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

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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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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

Contact center artificial intelligence (contact center AI) plays a pivotal role in enhancing the customer service experience through tools like chatbots, automated phone systems, intelligent email responses and more. This powerful technology has transformed how businesses interact with their customers — and organizations are adopting it at a record pace. According to research from Metrigy, over 70% of contact center leaders have indicated plans to ramp up their AI spending in 2024 and 2025.

What exactly is contact center artificial intelligence, and what should customer experience (CX) leaders consider when implementing it within their organization?

Contact center artificial intelligence refers to the application of AI technologies specifically designed to enhance and streamline customer service operations within contact centers. At its core, contact center AI aims to create a more intuitive, responsive and personalized customer service experience.

Contact center AI leverages subsets of artificial intelligence to understand, interact with and assist customers effectively. This includes natural language processing (NLP), which enables AI systems to understand, interpret and respond to human language in a meaningful and contextually relevant way. It involves several sub-tasks like tokenization (breaking down text into words or phrases), parsing (analyzing sentence structure) and semantic analysis (understanding the meaning of the text). In contact centers, NLP enables chatbots to comprehend customer inquiries, extract relevant information and generate appropriate responses.

Machine learning (ML) — a branch of AI that focuses on training algorithms to learn from data and improve their performance over time without being explicitly programmed — is also a critical component of contact center AI. ML algorithms can analyze vast amounts of historical data to identify patterns and trends. In customer service, machine learning can be used to predict customer needs, suggest next-best actions for agents and identify common issues. For example, if a significant number of customers are reporting a particular problem, ML algorithms can detect this trend and alert company leaders to take action.

There are many different applications of AI for the contact center. With the recent popularity of generative AI (GenAI) within customer experience operations, contact center AI capabilities are expanding exponentially.

Some examples of contact center AI in action include, but are not limited to:

Conversational bots: Also known as chatbots, these software applications simulate human conversation through text or voice-based interactions. They use natural language processing and machine learning to understand and respond to user queries or commands. To manage voice-based interactions, chatbots utilize automatic speech recognition (ASR) technology, which converts spoken language into text. In customer service, chatbots are effective for handling simple inquiries and tasks around-the-clock, reducing the need for direct interaction with human agents.

Agent assist bots: This technology provides real-time support to human agents by offering instant suggestions related to customer queries, retrieving customer data, automating routine tasks and more.

Robotic Process Automation (RPA): RPA involves using software robots, or "bots," to automate repetitive, rule-based tasks that human agents typically perform. Robotic process automation can handle various tasks, including data entry, transaction processing, report generation and customer record management. For example, if a customer updates their contact information in a self-service application, RPA can automatically capture this new information and update all relevant systems and databases in real-time.

Voice biometrics: Voice biometrics analyze voice patterns to authenticate users, considering factors like pitch, tone and speech rhythm. They can enhance security in the contact center by verifying a customer's identity through their voice, reducing the need for passwords or security questions.

Analytics and reporting: AI-driven analytics tools collect and analyze large volumes of data from customer interactions. One key application is sentiment analysis, which uses natural language processing and machine learning algorithms to identify and categorize emotions expressed in text or speech. By detecting positive, negative or neutral sentiments, sentiment analysis helps gauge customer emotions during interactions. This information can be used to prioritize responses, escalate issues to human agents when needed and tailor responses to enhance customer satisfaction. Insights like these enable businesses to understand customer behavior, identify trends, measure the effectiveness of support strategies and make informed, data-driven decisions.

Each tool within the contact center AI suite has its unique advantages, contributing to a more streamlined and effective contact center operation. 

Some of the most common benefits include:

According to the aforementioned research by Metrigy, the primary driver behind increasing AI contact center spend is the promise of efficiencies. By automating repetitive tasks such as answering FAQs, processing orders and gathering customer information, contact center AI applications like RPA and chatbots free up human agents to focus on more complex interactions.

For example, a large global technology company faced challenges with long average handle times and low customer satisfaction due to the manual data entry tasks required of its contact center agents. To address these issues and enhance both employee and customer experiences, the company engaged TELUS Digital to implement a custom RPA Solution. The solution incorporated feedback from frontline team members at the organization and involved connecting disparate systems with an application programming interface (API) to eliminate manual steps in the company's processes. Implementing RPA resulted in a 22% decrease in average handle time, a 35.3% increase in call capacity and an overall improvement in customer experience.

Contact center AI's enablement of self-service options, such as chatbots and interactive voice response (IVR) systems, also plays a primary role in enhancing efficiencies, as they empower customers to handle simple queries without engaging with a live agent, according to Max Ball, principal analyst at Forrester. "AI gives you a lot of power; it allows you to do more in self-service, which is really valuable," he said in the webinar Driving CX success with AI and automation in CCaaS, hosted by TELUS Digital. Ball further explained that the ROI of self-service is significant, making the implementation of AI in the contact center a "no-brainer." He emphasized that brands should capitalize on AI capabilities as much as possible to maximize their benefits.

And with contact center AI's ability to predict call volumes and optimize staffing levels, its efficiency benefits also extend to workforce management. AI can accurately forecast demand and adjust staffing levels by analyzing historical data and real-time interaction patterns. It can help ensure that the right number of agents are available during peak times, thereby reducing customer wait times and preventing overstaffing during slower periods.

When employees are equipped with the right tools, training and support, they can perform their jobs more efficiently and with greater satisfaction. This translates to more engaged and motivated agents who can better assist customers, resolving issues quickly and delivering exceptional service. Additionally, increased job satisfaction leads to lower employee attrition, reducing the need for costly retraining.

Contact center AI tools help reduce the cognitive load on human agents by providing real-time information, suggested responses and automated call summaries. Research by HubSpot shows that "78% of customer service professionals believe AI and automation allow them to focus more on the most important aspects of their roles."

For example, a growing consumer robotics company looking to maximize the performance of its customer support agents engaged TELUS Digital's automation experts to implement an agent-assist chatbot. By training the bot on information housed in the company's existing knowledge base, agents could look to the bot for help in navigating company and product information. The bot is capable of effortlessly surfacing the correct information, as well as tips, scripts and reminders. Implementing the agent-assist bot improved agent satisfaction by 37%, lengthened tenure for in-house agents and reduced the learning curve for new hires, decreasing the ramp training time for the client's growing CX operation.

With countless customer interactions occurring across multiple channels daily, there is a vast amount of data rich with essential insights waiting to be mined. AI-powered contact center analytics tools can analyze the data to identify trends, uncover insights and generate actionable recommendations, such as determining where to use chatbots, when to hand-off to the right agent and much more.

In the aforementioned webinar hosted by TELUS Digital, Ball noted that 100% of interactions in the contact center are recorded, but only a very small handful (1%) of those interactions are actually reviewed for quality assurance or other insights. "It was data that was just dead. It was too hard to get to, you had to listen to recordings and it didn't make sense to leverage it. There was no way to get value there."

But with AI making speech-to-text effective, 100% of contact center calls can now be transcribed, and all of those transcriptions, added Ball. This gives companies greater visibility into their contact center operations. "That data is now accessible; that data is now sharable. This is the real voice of the customer. The insights that are available here are huge."

While the benefits of contact center AI applications are significant, organizations can face challenges in their adoption and implementation. Understanding these obstacles is crucial for overcoming them effectively and ensuring seamless implementation. 

High initial costs: Implementing contact center artificial intelligence involves significant initial investments in technology, infrastructure and training.

Integration with existing systems: Ensuring that AI tools work seamlessly with existing customer relationship management (CRM) systems, databases and other contact center software is critical to avoid disruptions in service and data flow. However, some contact centers operate on legacy systems that may not be compatible with modern AI technologies. Integrating contact center AI into these outdated systems can be complex and costly.

Data quality and management: AI's effectiveness depends on the quality of data it learns from. Inaccurate or incomplete data can lead to poor performance and misguided insights. Moreover, some organizations may store their data in disparate systems, creating silos that hinder AI from accessing a comprehensive view of customer information.

Data privacy: AI systems require access to large volumes of data — sometimes including sensitive customer information — to function effectively. Ensuring this data is collected, processed and stored in compliance with stringent data protection regulations, such as GDPR, can be complex. Additionally, brands must safeguard against data breaches and unauthorized access, as any lapse in security could erode customer trust and result in significant legal and financial repercussions.

Change management: Introducing AI to your operations can lead to apprehensions among employees about job displacement and changes in their roles. Resistance to change can impede successful contact center AI (CCAI) implementation.

Optimizing the user experience: According to Statista, almost half (48%) of consumers said that if it made their CX with a brand more seamless, consistent and convenient,  they would interact with AI more frequently. With that said, ensuring that AI interactions are smooth, intuitive and effective is crucial. Poorly designed AI systems can frustrate customers and negatively impact their experience.

Balancing AI and a human touch: According to a report by Execs In The Know, sponsored by TELUS Digital, 40% of consumers listed "difficulty reaching a live person" as the most frustrating aspect of resolving a customer care issue with a brand. While AI can efficiently handle routine tasks, it lacks the empathy and nuanced understanding that human agents provide. Ensuring customers feel valued and understood requires a seamless integration where AI manages simple inquiries, while complex or sensitive issues are escalated to human agents.

Monitoring and maintenance: AI systems require continuous monitoring, fine-tuning and maintenance to perform optimally, which can be resource-intensive.

Implementing contact center AI can elevate customer service operations, however, to reap the many benefits of the technology, it's important to follow best practices that ensure a smooth and effective deployment. 

Define clear objectives: Before diving into implementation, outline your specific goals. Are you aiming to reduce call handling times, improve first-call resolution rates or enhance customer satisfaction? Clearly defined objectives will guide your AI strategy and help measure its success.

Start with pilot projects: Small-scale pilot projects are a great way to test the effectiveness of AI solutions in your contact center environment. Pilots allow you to gather valuable data, identify potential issues and make adjustments before a full-scale rollout. Choose a manageable segment of your customer interactions to start, such as handling common queries or support requests.

Ensure data quality and integration: As previously mentioned, high-quality data is the backbone of any AI system, making effective knowledge management essential. Knowledge management tools, practices and personnel play a crucial role in maintaining data integrity by ensuring that information is accurate, relevant and up-to-date. These systems help in organizing and curating the data that AI systems use, setting them up to deliver more accurate insights and recommendations. Integrating AI with existing CRM systems, knowledge bases and other relevant platforms enables seamless access to comprehensive customer information. This integration ensures that AI can interact with the most current and complete data, leading to more accurate and personalized customer interactions.

Keep humans in the loop: It's critical to ensure that the path from AI tools to speaking with a human agent is easily accessible, allowing customers to reach a real person without frustration or delays. Striking the right balance between AI and human agents involves carefully designing workflows so AI enhances, rather than diminishes, the customer experience, maintaining a personal and empathetic touch in interactions.

Focus on user-friendly interfaces: For AI to be effective, it must be user-friendly for both customers and agents. Design interfaces that are intuitive and easy to navigate. Train your agents to work alongside AI tools, leveraging their capabilities to provide better service rather than seeing them as replacements.

Prioritize continuous learning and improvement: AI systems learn and improve over time through machine learning and natural language processing. Continuously monitor AI performance, gather customer and agent feedback, and make iterative improvements. Regular updates and fine-tuning are essential to maintain high performance and adaptability to evolving customer needs.

Maintain transparency and trust: According to a TELUS Digital survey, over 70% of Americans said they expect companies to be transparent in how they use generative AI. Customers should be informed when they are interacting with an AI system. Transparency builds trust and sets clear expectations. "[Companies] should also disclose how they collect, use and protect customer data, as well as the benefits and risks of data sharing," wrote Michael Ringman, chief information officer at TELUS Digital in a Forbes article. "Clear and easy-to-understand privacy policies that explain customers' rights and choices regarding their data should also be communicated."

Invest in security and compliance: Implement robust security measures to protect data and comply with relevant regulations. Regular security audits and updates are necessary to safeguard against breaches and maintain customer trust.

Monitor metrics and key performance indicators (KPIs): Track metrics and KPIs such as customer satisfaction scores, average handling time and resolution rates to evaluate the effectiveness of your AI implementation. Data-driven insights will help you understand AI's impact on your contact center operations and identify areas for further enhancement.

Foster a culture of innovation: Engage your team in the AI implementation process, solicit their input and provide ongoing training and support. An empowered and informed team is more likely to leverage AI effectively, driving better outcomes for your contact center.

By following these best practices, you can ensure a successful CCAI implementation that enhances customer experience, boosts efficiency and drives long-term value for your organization.

The complexities involved in integrating AI into customer service operations demand expertise, strategic vision and a deep understanding of both technology and customer experience dynamics. Choosing an AI and automation partner to assist in designing, building and delivering your chosen CCAI tools is paramount to the success of your strategy. A skilled partner brings a wealth of knowledge and resources that can make the difference between a smooth, impactful deployment and a costly, ineffective endeavor.

Reach out to our team of experts to see how our deep domain expertise and cutting-edge AI-driven tools can help your brand streamline its CX operations and exceed your customer's expectations.

Discover how contact center AI can elevate your CX operations. Explore benefits, challenges, best practices and more.

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The ultimate guide to contact center AI

Enterprises worldwide are focused on optimizing customer experience (CX) delivery costs amidst challenging economic times.

Watch now to explore decision-making processes that reduce costs without negatively impacting CX.

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Enhance your CX strategy

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

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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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Leveraging automation for labeling large 3D pipelines

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