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.

Why Higher Ed Needs to Go All-in on Digital

In the wake of the COVID-19 pandemic, the majority of students within the 18-24-year-old demographic now expect hybrid learning environments–even once we are beyond the pandemic. And a vast number of adult learners are seeking options that accommodate their work and family lives now that it’s clear that effective learning can indeed occur virtually. Implementing cloud technologies and achieving digital maturity within higher education will enable institutions to be innovative and responsive to evolving student preferences, while being prepared for future disruptions.

The state of digital maturity

In February and March 2021, Boston Consulting Group (BCG), in partnership with Google, surveyed U.S. higher education leaders on their views of the state of digital maturity in the higher education sector. This survey found that institutional and technology leaders strongly agreed that moving legacy IT systems to the cloud, centralizing and integrating data, and increasing the use of advanced analytics is necessary to make a successful digital transformation, and ultimately achieve digital maturity.

But what is digital maturity? Digital maturity—a measure of an organization’s ability to create value through digital delivery—focuses on three areas of technological advancement that drive large-scale innovation:

1. Using cloud infrastructure
2. Expanding access to data
3. Using that data to improve processes through advanced analytics, such as Artificial Intelligence and Machine Learning (AI/ML)

Although university leaders agree on prioritizing digital maturity, more than 55% said they considered their schools to be “digital performers” or “digital leaders.” However, only 25% of tech leaders at these universities stated that their schools regularly use data analytics. As with corporations and governments, higher education institutions face barriers to technological innovation, such as:

• Competing priorities to meet step-change goals and decentralized decision making
• Budget constraints
• Cultural resistance to change
• Tech staff skillset gaps

Still, leaders understand that the way to overcome institutional inertia is with a strong, goal-oriented vision of what is best for the institution overall. Although only a handful of schools have reached digital maturity as we define it, others can learn a great deal from their examples. Here are the top takeaways from higher education leaders who successfully transformed their institutions:

Digital solutions can improve the student journey in many ways

As digital capabilities hold the key to dealing effectively with declining enrollment and rising costs, higher ed leaders identified four goals that are critical to improving performance:

1. Improve the student journey
2. Increase operational efficiency
3. Scale computing power in advanced research
4. Innovate education delivery

The research found that technology investments can help enhance the student journey in the recruiting and retention of students, improving digital education delivery, government funding, and donations from alumni. Digital maturity can make institutions more agile and efficient in delivering education that aligns with the changing societal norms, evolving student preferences, and future disruptions. Survey participants shared that they plan to increase the use of the cloud by more than 50% over the next three years. By shifting legacy IT systems to the cloud, institutions can increase scalability, lower the cost of ownership, and improve operational agility, while offering a more secure, long-term data storage solution.

Cloud-native software-as-a-service (SaaS) solutions provide an excellent platform for centralizing data. However, institutions that attempt to “lift and shift” their legacy systems to the cloud may encounter challenges to achieving measurable improvements in data integration and cost reduction. Higher ed leaders must realize that centralizing data and transitioning to the cloud do not happen simultaneously.

Leaders who are able to articulate a strong vision and commitment will experience a more successful technology transformation. By linking their vision to specific needs, such as more effective recruiting, leaders will find their technology investments will have a more substantial return. University presidents should base their decisions about which systems to move, when, and how on desired performance outcomes.

Big visions become a reality with small steps. Small pilot projects are an excellent way to start the journey toward digital maturity. Small steps toward a significant transformation can reduce resistance to change, build positive momentum, and produce better student outcomes. Read the full report here. If you’d like to talk to a Google Cloud expert, get in touch.