Simulation Operations: Accelerating the Path to the Age of Autonomous Systems
February 25, 2025
Introduction
The ultimate pursuit of a fully Autonomous System stretching from – Autonomous Vehicles (AVs) and unmanned Aerial Vehicles (UAVs - Drones) to Delivery and Manufacturing Robots, Micro-mobility, etc. has been a longstanding ambition for humanity. Achieving this steep goal necessitates overcoming significant Engineering, Regulatory (Policy), and Safety challenges. While we surely are moving in the right direction and this ambition is achieved by some on the playing field, it remains a very interesting problem for the rest to solve.
Simulation is one of the most effective tools in developing and validating an Autonomous System. All Autonomy applications rely on a strong verification and validation strategy for a commercially viable product, with Simulation as the backbone. Broadly speaking, this encapsulates creating simulated representations of the physical world to build the Autonomy AI. The complexity lies in the levers of simulated realism, scalability as a function of cost and compute, and ease of creating a parameterized space to extract the signal of interest (amongst many others).
In this post, we explore how Human in the Loop Workflows (HiTL) expedites adopting this Simulation tool to build maximum test coverage for safer, reliable Autonomous Systems. We will look back on the history of Simulation, key components of the Sim-eng-ops ecosystem, present-day trends in foundational models, building effective Simulation Operations, and how these aspects connect to speed up meaningful product development.
A Brief History of Computer Simulations in the Automotive Industry
Computer Simulations have played a pivotal role in engineering disciplines since the mid-20th century, initially emerging in safety-critical fields such as Nuclear Physics (defense tech) and Aerospace Engineering. The Automotive industry quickly followed suit and adopted simulation techniques to enhance design and safety testing. Before the introduction of computational methods, crash testing relied solely on physical prototypes, which were costly, time-consuming, and often destructive.
The advent of Finite Element Analysis (FEA) in the 1960s and 1970s revolutionized vehicle safety testing by enabling virtual crash simulations. By leveraging FEA, engineers could model complex material behaviors and simulate crash scenarios, leading to several cost reductions, increased efficiency, and enhanced insight.
It may surprise you to learn that some of the crash simulations required overnight computer runtimes to produce results for a single iteration in the 1980s (Haug et al., 1986). This is impossible to imagine in the current era of unlimited GPU and Quantum Computing power. As computational power exploded, simulation methodologies evolved to include multi-physics modeling, near-real-time processing, and machine learning-enhanced neural modeling. These advancements have minimized barriers to entry for simulation and paved the way for a quicker integration into Autonomy Systems and similar Physical AI development.
Trends in Physical AI Foundational Models
With advancements in silicon chip design, computing power, and network speeds: we are at the cusp of a revolution in the usage of Simulation. This is similar to the inflection point in cloud computing spend, which grew 10x in the last 10 years (Link). Reports from the National Bureau of Economic Research (NBER) indicate that the prices of basic cloud services fell at double-digit annual rates between 2014 and 2016. The rate of decline has reduced but overall prices have continued to have a downward trend due to technological evolution and higher adoption.
Let’s draw an analogy between these two massively adopted technologies: Cloud Computing and Simulations. The Cloud Computing landscape has 3 primary categories:
Cloud Service Providers: Led by AWS, Microsoft Azure, and Google Cloud Platform (GCP)
Application Layer: B2C (Netflix, Zoom, Ube,r etc.) and B2B (Databricks, Shopify, Workday, etc.) players building applications on Cloud
System Integrators: B2C service providers helping corporations adopt cloud computing (Accenture, Capgemini, TC,S etc.) for their internal and external needs.
Fig 1: Cloud Industry Structure
Similar to Cloud Computing, the landscape of Simulations is becoming clearer due to the development of underlying infrastructure. The last few years have witnessed the launch of multiple foundational models that act as core simulation engines.
To note a few companies championing this:
NVIDIA's Cosmos platform (launched in Jan 2025): The openness of Cosmos’ state-of-the-art models unblocks physical AI developers building robotics and AV technology and enables enterprises of all sizes to more quickly bring their physical AI applications to market. Developers can use Cosmos models directly to generate physics-based synthetic data, or they can harness the NVIDIA NeMo framework to fine-tune the models with their own videos for specific physical AI setups.
PD Replica Sim by Parallel Domain: PD Replica Sim allows AV companies to recreate simulations from their own capture data in near-pixel-perfect scene reconstructions and create fully annotated, simulation-ready environments with unparalleled realism and variety.
Meta’s Habitat 3.0 (launched in Mar 2024): Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in indoor and home environments.
These models address critical challenges in physical AI development, such as data scarcity, high computational costs, and safety concerns. The ability of such platforms to generate realistic, physics-based synthetic data and their support for efficient model customization makes them a valuable asset for developers aiming to advance the capabilities of autonomous systems and robotic applications.
It is unclear at this point what the leaderboard for physical AI foundational models will look like in 10 years. We can definitely crystalball a trend where other players will jump on board; and use these models to build platforms and applications making Simulation a modular off-the-shelf capability for verifying Autonomy Systems. The industry structure in the future will shadow the cloud ecosystem with the following players:
Foundational AI Model Developers: Companies such as NVIDIA, and Meta will create foundational physical AI models
Sim Platforms/Tool Developers: Companies who will create platforms for Sims adoption. Some of the current cloud platforms such as AWS are already creating such services.
Sim Apps Developers: Specialised companies who will build applications for specific use cases such as on-demand Sim Generation, Sim Lifecycle Management, etc.
Sim Integrators: Companies who will perform the task of last mile adoption by creating an effective and efficient workforce for system integration, running SIM operations and workflows.
Fig 2: Sim Industry Structure
With the advent of sim-in-the-loop development, we are about to experience breakthrough improvements in the following area
Safety & Test Coverage: Simulation allows for testing dangerous scenarios without risking human life or property. It enables developers to identify and address potential safety issues early in the development process.
Accelerated Development Cycle: Simulating scenarios is significantly faster and cheaper than real-world testing. It avoids the need for physical prototypes, test tracks, and associated logistical expenses. This accelerates the development cycle.
Scalability and Repeatability: Simulations can be easily scaled to run thousands or millions of scenarios concurrently. The same scenarios can be repeated consistently, allowing for rigorous testing and comparison of different algorithms and software versions.
Some of the second-order benefits of simulation adoption include
Innovation & Creativity: With reduced cost of adoption, simulation will not be reserved for large megacorps. With the increased democratisation of this technology, we will be witnessing new products, business models, and academic pursuits.
Safety as a Core Tenet: By accelerating the physical AI development cycle, Simulations can create a safer future both from existing problems (e.g. car accidents, industrial accidents); and also create a framework of safety for any new product development. This will inherently prioritize safety as a core tenet of any physical product development.
At DDD, we feel that a system integrator/operator will be required to accelerate and democratize the use of Simulation for companies trying to build autonomous products. With our vast experience in Model Training, Safety Review, and Triage Operations serving L4+ AV customers, we are confident to fit into this role seamlessly.
Double Click on HiTL Simulation Operations
Now that we have a good understanding of the Simulation landscape, let us dive a little deeper into Simulation Operations. Simulation Operations refers to the structured orchestration of simulation workflows, tools, and infrastructure to support large-scale, data-driven autonomous system development. Unlike traditional simulation approaches, Simulation Operations emphasizes automation, scalability, and integration across multiple domains. Key components include:
Sim Suite Management
As companies scale their test operations and developer ecosystem, it becomes critically important to manage offline testing modality to provide a maximum ROI and seamless experience. Simulation Suite Management encompasses the application of specialized tools, processes, and practices to organize the simulation macro (input tests, output data, result conclusions) in easy-to-interpret constructs. It includes the following broader areas:
Scenario creation, editing, and augmentation overlay
Scenario expiration, and its lifecycle management
Aggregate sim suite health and status reporting
Adversarial Testing - rare but critical failure scenarios, such as GPS outages or sensor malfunctions
Centralized data access: Cloud-based platforms for seamless team interactions.
Standardized metrics: Common performance benchmarks and reporting structures.
Stakeholder engagement: Transparent reporting mechanisms for regulatory bodies and safety auditors.
Sim Creation
Simulation creation is the process of generating virtual environments and scenarios to train, test, and validate the behavior of autonomous systems. It involves creating realistic digital replicas of the real world, including roads, traffic, pedestrians, weather conditions, and other relevant factors. These simulations allow developers to evaluate the performance of autonomous systems in a safe and controlled environment, without the risks and limitations associated with real-world testing.
There are broadly following ways in which Sims are created:
Synthetic Sim Creation: This involves creating virtual environments from scratch using foundational models, computer graphics, and 3D modeling techniques. It allows for a high degree of control and customization but can be time-consuming and may not always capture the full complexity of the real world.
Log-based Sim Creation: This approach uses real-world data, such as sensor logs from autonomous systems or recordings of human usage behavior, to recreate specific scenarios in a virtual environment. It can be more efficient than synthetic simulation and ensures that the simulated scenarios are realistic, but may be limited by the availability and quality of the data.
Digital Twin Validation
Digital Twin is a virtual replica of a physical object, system, or process that accurately mirrors its real-world counterpart's behavior, and performance, and even predicts its future behavior. Digital twin validation is the process of making sure that a digital twin accurately reflects the real-world object or system it represents. It's a correlation analysis that provides a higher degree of confidence in the virtual environment for scaling up any V&V activity. In addition to AV use cases, this process is widely used in robotics, aerospace, defense, and any safety-critical system analysis.
Sim Results Analysis & Reporting
Sim Results Analysis & Reporting is the process of extracting meaningful insights from simulation data and communicating those findings effectively. It's a critical step in any simulation project, as it allows you to understand the behavior of the system being modeled and make informed decisions based on the results.
The integration of Simulation Operations into Autonomous Systems development accelerates progress by addressing critical industry challenges such as safety and risk mitigation, scalability, and cost-effectiveness. The industry trend indicates that a well-defined end-to-end Simulation Operations expertise will turbocharge the development cycle for autonomous products.
Conclusion
Just as simulation transformed automotive crash testing, Simulation Operations is revolutionizing the development of autonomous systems. By providing a scalable and automated framework for testing and validation, and end-to-end Simulation Operations offering accelerates the deployment of safe and reliable technology. As computational capabilities continue to advance, the integration of AI-driven simulations and real-world validation will further refine AV technology, pushing the boundaries of automation and safety. The future of Simulations is also exciting - innovations such as Neural Sims, which can generate multiple simulation environments from one solitary log can multiply the effectiveness of simulations. In conclusion, the future seems bright - the age of Physical AI is imminent and Simulations will unlock the doors to that age.
DDD has positioned itself to be at the forefront of this revolution and contribute to ushering in the Age of Autonomy Systems. To learn more talk to our simulation experts.
References
Belytschko, T., Liu, W. K., Moran, B., & Elkhodary, K. (2000). Nonlinear Finite Elements for Continua and Structures. Wiley.
Haug, E., T. Scharnhorst, P. Du Bois (1986) "FEM-Crash, Berechnung eines Fahrzeugfrontalaufpralls", VDI Berichte 613, 479–505.
Kalra, N., & Paddock, S. M. (2016). Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? RAND Corporation.
Koopman, P., & Wagner, M. (2017). "Autonomous Vehicle Safety: An Interdisciplinary Challenge." IEEE Intelligent Transportation Systems Magazine, 9(1), 90-95.
UniSim: A Neural Closed-Loop Sensor Simulator, CVPR 2023 - Ze Yang, Yun Chen, Jingkang Wang, Siva Manivasagam, Wei-Chiu Ma, Anqi Joyce Yang, Raquel Urtasun
