Volume XI - Summer 2020 | mscsoftware.com | 77

Smart virtual testing for smart

autonomous vehicles

By Dr. Michael Schlenkrich and Dan Marinac Hexagon | MSC Software

Autonomous

Volume XI - Summer 2020 | mscsoftware.com | 77

With advances in sensor technology and real-time artificial intelligence, autonomous vehicles (AV) are creating a new transportation experience for us all. Our vehicles are becoming smarter through the introduction of advanced driver-assistance systems (ADAS) and autonomous driving functions (Figure 1). These new technologies are forcing new business models pertaining to improving traffic flow, crash-avoidance, reducing pollution, and occupant stress. Testing and verifying these new levels of intelligence in vehicles represents a growing challenge.Learn more about Volume Graphics:

www.volumegraphics.com

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As autonomous driving becomes a reality, the onus is on the OEM’s software developers to deliver a safe, efficient, comfortable driving experience. In smart cars, everything boils down to software and sensors. There is an ever-increasing complexity in the interaction between the different systems in the vehicle. What is of paramount importance is the interaction of systems such as sensory (optical, Lidar, radar, ultrasound sensors and the driver/controller logic), environment (light, weather, obstacles, pedestrians, road and traffic conditions), and the physical behavior of the vehicle (structural, crash, noise, vibration, handing, ride, comfort, durability, fatigue). The possible combinatory of what needs to be tested and validated is growing exponentially. Waymo recently announced that its autonomous cars have driven tens of billions of miles through computer simulations and 20 million miles on public roads in 25 cities, including Novi, Michigan; Kirkland, Washington; and San Francisco (1). One realization has become clear: New smart systems (ADAS and AV functions) cannot be fully tested and validated through physical tests; simulation will become the primary test and validation method.

Hexagon is uniquely positioned as the industry’s leader in providing systems for automotive OEM’s to build autonomous vehicles.

Hexagon Manufacturing Intelligence together with Hexagon Autonomy & Positioning are global technology leaders pioneering autonomy and positioning solutions (Figure 2).

Hexagon’s Autonomy & Positioning division leverages positioning technology and products from NovAtel, and AutonomouStuff to bring together cutting-edge positioning solutions for the autonomous industry. NovAtel designs and manufactures high precision OEM positioning technology to precision agriculture, defense, transportation, marine and survey industries.

AutonomouStuff enables users to accelerate the development and deployment of their driverless applications. Leica Geosystems provides 3D reality capture solutions combining a high-performance laser scanner and mobile-device app to capture and register scans in real time. Vires from Hexagon Manufacturing Intelligence provides Virtual Test Drive (VTD) and VTD Scale which incorporates all the elements required to conduct the required virtual testing of AV and ADAS functions.

Figure 1 : Vehicles are becoming smarter through the introduction of advanced

Figure 2 : Hexagon Manufacturing Intelligence together with Hexagon Autonomy & Positioning

driver-assistance systems (ADAS) and autonomous driving functions.

are global technology leaders pioneering autonomy and positioning solutions.

Smart Autonomous vehicles create a unique set of opportunities and challenges. In order to test drive billions and billions of virtual miles through computer simulations, cloud-based computing services are required. These cloud-based data centers enable thousands of CPUs and GPUs to perform 100,000’s of parallel simulations to build, test, deploy, and manage these complex simulations. Even with this massive computing power, engineers need to be smart to address the most relevant scenarios with a credible range of parameters to model these instances. They must envision scenarios that ADAS might encounter to ensure a broad range of possibilities (parameters) that would capture edge cases, where the systems are the most challenged. Edge cases are problems or situations that occurs only at an extreme (maximum or minimum) of the operating parameters. Compounding this issue is the fact that the relevance of a scenario and the edge cases change as design of the AV and ADAS change. It is not enough to simply create a fixed set of scenarios to test the designs, it is paramount that the test scenarios (populations) need to be constantly evolving with the design. As such, any system needs to be tightly integrated into the whole continuous deployment / continuous integration framework of the ADAS/AV functions. Finally, we need to balance the speed of simulation and the fidelity of the simulation. For example, we don’t want to model the vehicle with only 50-100 degrees of freedom (DOF), when we know in fact that there are thousands of DOF to truly capture the ride and handling effects. It is essential that the test and validation system provides high confidence that the simulation delivers the same behavior as the real physical system would generate under the same conditions.

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This is important not only for the ideal system, but also for a real world (digital twin) system where the performance of each individual components varies due to manufacturing and fatigue effects.

VTD Scale

VTD Scale is a cloud-native solution, which provides a full environment to conduct large scale virtual simulation of AV and ADAS functions with the industry leading simulation tool VTD (Figure 3). The full functionality of VTD as well as its unmatched integration capabilities with customer components and 3rd party application is fully utilized in the cloud.

VTD Scale offers a consumption-based licensing model which encourages parallel execution achieving maximum performance with minimum compute time, all without cost penalties.

Figure 3 : VTD Scale is a cloud-native solution, which provides a full environment to conduct

Figure 4 : Simulate billions of miles faster than real-time simulations and enable increased

large scale virtual simulation of AV and ADAS functions with the industry leading simulation tool VTD.

speed to deployable systems.

The scope of VTD Scale encompasses:

• Parametrics: Parametric definition of scenarios that capture all aspects of the environment (static and dynamic) of the System Under Test (SUT), including sensor and controller parameters. VTD Scale has a flexible framework which can apply parameters to both the simulation model and scenario. Parameters can be directly mapped to fields in any model-data file (for both environment and SUT) and generative applications, which parametrically create on-the-fly simulation model files. This permits parametric control of static content (such as road decoration) and dynamic content (such as placement of pedestrians, obstacles, etc.).

• Scripting/Automation: VTD Scale utilizes Python to allow the creation of any study sequence (template), allowing the integration of open and closed-loop sampling methodology. VTD Scale supports more advanced approaches beyond the traditional (single step) Design of Experiments (DOE) or stochastic sampling strategies. This allows the inclusion of Artificial Intelligence / Machine Learning based methodologies to identify and focus on edge case subspaces (sub-populations) during the study.

• Cloud standards: VTD Scale utilizes standards (based on Kubernetes) to enable large-scale simulation, in which 1000’s of containerized simulation flows based on the VTD software are executed in parallel (Figure 4).

• Full Integration: VTD Scale has a flexible framework to link together VTD with other containerized services (sidecars); which include the sensor, controller and physical vehicle simulators. The full richness of the VTD communication mechanisms (such as FMI) is supported. VTD Scale can manage different versions and model fidelities of the components. During the VTD Scale studies, the components of the simulation can dynamically be compiled to the setup for a co-simulation.

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• Integration of Adams Car: VTD Scale provides both simple (in VTD) and high fidelity (with Adams Car) vehicle models. • Any road network: VTD Scale can be used both with simple (synthetic) road networks, and with large geo-specific road

networks (digital twins). • CPU and GPU: VTD Scale can support (fully scalable) both CPU-based and GPU-based simulations.• Streaming Data Processing: Simulation processing is executed parallel, which allows to direct creation and

compression of data such as KPIs and annotations. It is crucial to keep the result data volume in bounds and prepare the data for immediate access to the data analytics tool. This enables direct creation and training data for any type of supervised or unsupervised training (Machine Learning).

• Open System provides access to any Data Analytics tool: VTD Scale presents results in standard data structures which though the use of data adaptors, offers connection to all data analytics tools. This allows engineers and data scientists to directly (with no further processing or data movement) process and analyze the study results.

Figure 5 : Rapidly evaluate and classify edge scenarios with VTD Scale.

Example of a VTD Scale based study template

The flexibility of VTD Scale and the richness of VTD allow the creation of very sophisticated simulation studies. These studies typically require manual intervention. The design and development of VTD Scale supports simulation study templates, where both the need for speed of exploration and the need to certainty of the simulation results are required and should be combined.

Many studies consist of two phases:

• Exploration phase, where with smart methods, a scenario space is explored, and the edge case scenario population is identified (Figure 5). The objectives of the edge cases can differ significantly, as not only those are of interest (which create unsafe/risky situations), but also those where the driving experience for the passengers is unpleasant (comfort factor) or circumstances where the sensors will have problems (such as fast light/dark switches with optical sensors).

• Those edge case populations can then be followed up with higher fidelity simulations (requiring potentially significantly higher compute demands), to assess with confidence the behavior of the system and conduct the final performance evaluation (Figure 6).

VTD Scale architecture allows for these complex flows, which will require creating setups with different model fidelity, handling both CPU-based and GPU-based models, and incorporating different sampling and evaluations directly in the simulation template, including the use of external tools.

Summary

VTD Scale is the logical progression of VTD. It offers a cloud enabled modern service-based architecture for testing autonomous driving scenarios. It offers both CPU and GPU-based scalability with large parallel execution to maximize performance and minimize solve time. It provides all the key capabilities of Virtual Test Drive (VTD), but with the expansive compute power available in the cloud. VTD Scale presents a cost-effective consumption-based licensing model which encourages parallel execution. It incorporates the world class Adams Car analysis as a high-fidelity vehicle dynamics simulator. The system allows for the creation of very flexible study templates, which can then be executed fully integrated in the continuous integration and deployment (CI/CD) environment. VTD Scale performs the necessary results processing to keep the result data volume in bound. It is easily coupled with the preferred data analytics to analyze, dashboard and report the results.

Figure 6 : Extract and detect edge cases after running thousands of scenarios simulated with VTD Scale.

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For more information on VTD Scale contact your nearest MSC Software Office or reseller