Volkswagen strives to design beautiful, performant, and energy efficient vehicles. This entails an iterative process where designers go through many design drafts, evaluating each, integrating the feedback, and refining.

For example, a vehicle’s drag coefficient—its resistance to air—is one of the most important factors of energy efficiency. Thus, getting estimates of the drag coefficient for several designs helps the designers experiment and converge toward more energy-efficient solutions. The cheaper and faster this feedback loop is, the more it enables the designers.

Unfortunately, estimating drag coefficient is an expensive and time-consuming operation that involves either a physical wind tunnel or a computationally intensive simulation. This can be a bottleneck in the feedback cycle.

For this reason, Volkswagen and Google Cloud decided to collaborate on a joint research project to investigate using machine learning (ML) to get fast and inexpensive estimates of the drag coefficient. In this post, we’ll explore the challenges and approaches undertaken in this project.

The core principles of the project were simple. First, we needed to collect a dataset of existing car designs and their respective drag coefficients. Then, we needed to create a representation of the various cars that would be suitable for ML. The next step was to train a deep learning model to predict the drag coefficient, and then, finally, we would use that model to efficiently estimate drag for any new design.

Representing three-dimensional car designs

Design software recreates a physical object as a three-dimensional triangle mesh made up of three types of objects—faces, edges, and vertices. Figure 1, below, shows such a mesh for an Audi S6. Faces are flat surfaces, such as the window in a car door. An edge is where two faces meet (e.g., the side of the door), and a vertex is where two or more edges meet, such as the corner of the door.

Car bodies, however, come in all shapes and sizes. A Volkswagen Golf economy model is very different from a Tiguan SUV, and a single vehicle can have both large smooth surfaces as well as areas with delicately designed features. Consequently, there can be a huge variety from one polygonal mesh to the next.

ML models need consistent representation in order to form robust generalized rules. With such a dramatic variance between each polygonal mesh, the models would be compromised and the results could have huge margins of error.

We needed to find a way to create simple meshes that capture the shape of the car but are still suited for ML models.

Representing a car with digital shrink wrapping

Rather than building a representation of each car from the ground up, we applied a “shrink wrapping” method for the 3D meshes. The principle is very similar to vacuum-sealing a cucumber. The cucumber is placed in a plastic bag and the air is then gradually removed until the bag fits tightly around it, capturing its shape.

Our approach works similarly: we start with a base mesh, a simple shape that corresponds to the plastic bag, and we deform it until it captures the shape of the target mesh. For our purposes, the base mesh is a simplified representation of a car and the target mesh is the particular car we are designing for at that moment. Such meshes can be defined, managed, and presented to ML models for training using the Tensorflow Graphics and trimesh libraries.

Our “shrink wrapping” method mainly works by iteratively minimizing a measure of distance (e.g., chamfer distance) between the two meshes. Additionally we can regularize our mesh to preserve certain qualities, like smoothness, in the resulting mesh. This iterative optimization is analogous to the vacuum pump, gradually shrinking and fitting the vertices of the mesh as closely as possible to the complex shape of the car. With shrink-wrapping, we are able to produce cleaner meshes that are more suitable to our estimation task. An example of such a procedure is shown in Figure 2.

How to train a model

Shrink-wrapping the 3D car designs was an important first step, but the work was far from over. Our next challenge was to build and test the machine learning algorithms.

We wanted our algorithms to estimate the drag coefficient as accurately and quickly as possible each time it looked at a new design. To do so, we had to train the ML models on existing data.

From publicly available datasets, we calculated the drag coefficients for 800 different car meshes, which we trained the models on. Then, we evaluated the trained models on a further 100 meshes, seeing how accurate their estimates were on new data.

As we worked through this training, we refined our approach. Initially, we tested models based on convolutional neural networks – similar to PointNet – that observed only the vertices, i.e., the fixed points in each mesh. But when we tested mesh-convolutional models – similar to FeastNet – we found a slightly different focus improved the accuracy of the estimates. Rather than focusing on vertices alone, these models looked at a mesh of vertices and how they relate to each other. These models placed each vertex in a richer context, leading to more accurate estimates when air-flow hit particularly subtle design features.

Working in parallel and at scale
To collaborate across time zones and two organizations, we’ve used the Google Cloud Vertex AI platform.

Vertex AI Workbench serves as a central hub to interact with other services and infrastructure on the Vertex AI platform. It enables quick experiments and preparation of training packages for resource-intensive ML model training jobs, all in a Python notebook environment for immediate execution of code. The notebook environments allow code-based interaction with other services on Google Cloud and ML tools such as Vertex AI Training and Vertex AI Pipelines.

The process of training a new model is a seamless one. First, a dataset is prepared and stored in Google Cloud Storage, usually with the help of Tensorflow Datasets. Then, for every ML model we want to test, we package and store the training code as a container image with Google Cloud Build and Container Registry. This ensures that every job is fully documented, including the provided parameters, training code package, logs from the training task, and resulting artifacts such as metrics and model files.

From there, we submit the model to the Vertex AI Training service, which provides easy access to large scale infrastructure and hardware accelerators, such as GPUs and TPUs, by simply defining resource needs when submitting a job. By using Vertex AI Training’s hyperparameter tuning feature, we can run experiments in parallel with multiple neural networks to find the right one for our purposes.

With Vertex AI Tensorboard, we can capture metrics and visualize the results of our experiments. These are readily available to anyone in the team, wherever they are in the world, for a wider discussion.

The first milestone

This joint research effort between Volkswagen and Google has produced promising results with the help of the Vertex AI platform. In this first milestone, the team was able to successfully bring recent AI research results a step closer to practical application for car design. This first iteration of the algorithm can produce a drag coefficient estimate with an average error of just 4%, within a second.

An average error of 4%, while not quite as accurate as a physical wind tunnel test, can be used to narrow a large selection of design candidates to a small shortlist. And given how quickly the estimates appear, we have made a substantial improvement on the existing methods that take days or weeks. With the algorithm that we have developed, designers can run more efficiency tests, submit more candidates, and iterate towards richer, more effective designs in just a small fraction of the time previously required.

Going forward, faster and more accurate estimates could even enable more automated searching for efficient designs, which would help both engineers and designers to hone in on the areas of the vehicle body where they could have the most impact. An important next step will be integrating the results into 3D design software to let designers benefit from the output and provide feedback.

As we continue, our focus is on improving the accuracy of the models. Firstly, we will build a larger, better quality dataset. Secondly, we will improve our shrink-wrapping algorithm to capture more details. Finally, we will enhance our existing models by experimenting with Vertex AI Neural Architecture Search to explore and experiment with different neural architecture options.

Moreover, we believe that our results for drag coefficient estimation is only a starting point for further exploration. There could potentially be numerous use cases in the space of physical simulations and assessments where cost and time savings could be achieved through ML-based estimators.

Acknowledgements

This work wouldn’t have been possible without the contributions from Volkswagen Data:Lab, Google Research, and Google Cloud. Thanks to Ahmed Ayyad, Dr. Andrii Kleshchonok, Dr. Daniel Weimer, Gülce Cesur, Henrik Bohlke, Andreas Müller from Volkswagen, Ameesh Makadia, Ph.D., and Carlos Esteves, Ph.D., from Google Research, and Daniel Holgate, Holger Speh, and Dr. Michael Menzel from Google Cloud.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}

We’re excited to share the news that leading global research and advisory firm Forrester Research has named Google AppSheet a Leader in the recently released report The Forrester Wave™: Low-code Platforms for Business Developers, Q4 2021. It’s our treasured community of business developers—those closest to the challenges that line-of-business apps can solve—who deserve credit for not only the millions of apps created with AppSheet, but also the collaborative approach to no-code development that has helped further our mission of empowering everyone to build custom solutions to reclaim their time and talent. 

AppSheet received the highest marks possible in the product vision and planned enhancements criteria, with Forrester noting in the report that “AppSheet’s vision for AI-infused-and-supported citizen development is unique and particularly well suited to Google. The tech giant’s deep pockets and ecosystem give AppSheet an advantage in its path to market, market visibility, and product roadmap.”

“Features for process automation and AI are leading,” remarking that, “The platform provides a clean, intuitive modeling environment suitable for both long-running processes and triggered automations, as well as a useful range of pragmatic AI services such as document ingestion.”

Many enterprise customers including Carrefour Property – Carmila, Globe Telecom, American Electric Power, and Singapore Press Holdings (SPH) choose AppSheet as their business developer partner, along with thousands of other organizations in every industry. We are honored to serve these customers and to be a Leader in the Forrester Wave™: Low-code Platforms for Business Developers. We look forward to continuing to innovate and to helping customers on their digital transformation journey. To download the full report, visit here and enter your email address. To learn more about AppSheet, visit our website.

We meet daily with contact center owners and customer experience (CX) execs across all industries, geographies, and business sizes. Looking back at these conversations, it’s crystal clear that 2022 was a high-stakes year for call centers, with three primary challenges trending across all customers and continuing in 2023:

• Many organizations feel pressure to rapidly scale up their call center operations in response to macroeconomic changes. Uncertain conditions are forcing Contact Centers to be ever more cost-effective, and to find ways to generate revenue for the business.
• End users are increasingly demanding and less forgiving when it comes to CX. Users have a choice, and they expect brands to meet them where they are with superior experiences. Connecting with customers where they engage is one of the key components of superior CX—customers should not have to go through elaborate processes or unhelpful phone trees to get help but should rather have service available quickly and easily in their preferred channels. Consumers demand more intimate ways of connecting with brands and Conversational AI can create that critical interaction medium. 
• Organizations understand that AI can help address these challenges. However, many business leaders remain unsure how to successfully make the journey. A growing number of offerings are on the market, but many don’t deliver on their promise, with long and expensive integration requirements and unpredictable and underwhelming outcomes.

Helping our customers successfully address these challenges and opportunities was one of our top priorities last year and will continue to be a significant focus in coming months. In this blog post, we’ll review our Contact Center AI (CCAI) news from last year, as a primer for 2023. 

Looking back: Why 2022 was a big year for Contact Center AI

In 2022, we increased our strategic investment in CCAI, including expanding it to include a comprehensive, end-to-end contact center solution suite that is user-first, AI-first, and cloud-first. We launched Contact Center AI Platform, our Contact Center as a Service (CCaaS) offering, as part of the CCAI product suite that offers a modern, turnkey solution, designed with user-first, AI-first, and cloud-first design. During Google Cloud Next ‘22, we shared lots of great content on how organizations can use CCAI to improve customer experiences, including these breakout sessions:

• Delight customers in every interaction with Contact Center AI
• Power new voice enabled interfaces with applications with Google Cloud’s speech solutions

We also got a chance to hear how customers are using CCAI to better reach their own customers, including Wells Fargo and TIAA. We partnered with CDW to discuss Providing Better Customer Experiences and with Quantiphi in a webinar called “Elevating the Banking Experience with CCAI Platform.” Just recently, our customer Segra shared their success story.

Through these customer interactions, three key priorities have surfaced as we look forward to 2023: Elevate the customer experience, bring new forms of AI to drive new automation and accelerate time to value.

Looking forward: Elevate CX, integrate new forms of AI, accelerate time to value 

1. User-first: Meet them where they are with elevated Customer Experience.

As we have learned, users expect that brands meet them where they are and on their own terms and expectations. To do that, brands must integrate with and adopt the latest user-centric technologies and product best practices from consumer mobile and web apps. Enterprise B2C can’t exist anymore in a parallel world of different and often inferior user experience. Google has over 20 years of experience in building such consumer experiences, with multiple products successfully serving billions of users. Bringing these capabilities and experiences from our consumer products and research teams to our cloud offerings was a key component for our product offerings in 2022 and is a big part of our key investments in 2023. Moreover, a vast majority of CX user journeys start with a query on Google Search or YouTube. Connecting with the users at that point, even before they reach out directly to the contact center is a win-win, saving money for the brand and delivering immediate value to the user. By focusing on the user we created a superior  integrated omnichannel experience.

2. AI-first and cloud-first: Quality contact center growth depends on transforming to modern, Cloud, AI solutions.

For contact centers to evolve, they need to transform from cost centers to revenue generators. That requires modern Cloud and AI solutions. Conversational data spans across all parts of the contact center, opening new ways to generate value. Cloud capabilities of privacy, security and scale can enable personalized CX across channels, enabling key omnichannel experiences. From a study by McKinsey: “Cross-channel integration and migration issues continue to hamper progress. For example, 77 percent of survey respondents report that their organizations have built digital platforms, but only 10 percent report that those platforms are fully scaled and adopted by customers. Only 12 percent of digital platforms are highly integrated, and, for most organizations, only 20 percent of digital contacts are unassisted.” Traditional telephony technologies are becoming commoditized and struggle to keep up with ever more complex rule based systems. Leaders in applicative AI and Cloud technology are stepping up as the new partners for brands who understand they need to take the leap to the next generation CX solutions. .  

3. Accelerating time to value while future proofing investments with predictable and measurable value

Reducing upfront implementation investment and accelerating time to value can be a challenge for contact center solutions. Scaling Cloud and AI can provide a faster path advanced conversational AI, can help address these challenges. Let’s look at three examples:

• Out of the Box(OOTB) integrated transcription, chat and voice summarization, and topic modeling — This saves customers money by reducing agent handling time for every chat and call, as well as providing valuable insights that can be used for quality management, contact center optimization and automation, agent and user churn prediction, business insights, and revenue opportunities.
• AI based chat and voice calls steering  paired with info-seeking virtual agents — Together these deliver higher Customer Satisfaction at scale while reducing cost –  by significantly reducing waiting queues and being routed to the wrong agent, as well as automating away total handling time.
• Reduced time to full automation — Reduce the complexity of conversation modeling, prebuilt components and APIs for shorter time to value and more predictable outcomes, and metrics driven ML-Dev & QA tools and playbooks.

With these new capabilities, our customers can now see results as soon as they implement CCAI. We’re excited to get our customers to where they want to be faster!

And there you have it: a quick overview of CCAI and its progress in 2022 and what’s coming in 2023. For more details, check out the documentation or our CCAI solutions page.