What Is Sensor Simulation?

Sensor simulation models the physics and behavior of real-world sensors as they perceive their surroundings. It enables developers with a safe proving ground to train, test, and validate physical AI models for robotics, automotive, and industrial applications.

Autonomous systems such as robots and autonomous vehicles (AVs) rely on complex multidimensional AI models that use sensor data to perceive and react to their surroundings.

Developing physical AI algorithms for autonomous systems requires vast amounts of data to represent the diversity and unpredictability of real-world conditions. However, collecting and annotating large amounts of useful real-world sensor data is time-consuming and costly. Additionally, data for dangerous scenarios cannot be collected in the real world due to safety concerns.

Sensor simulation provides a safe, controllable, and scalable way to train, validate, and test data-hungry models and accelerate the development of physical AI.

Sensor simulation renders physically based environments where developers can run autonomous models through countless lifelike “what-if” scenarios for robust training and testing. This is especially critical for robotics, AVs, and smart factories, where precision in sensor data and interaction with the environment is crucial.

Simulated sensors in virtual environments streamline the development for physical AI in a number of ways:

Simulations can be used to generate synthetic data and new variations that can capture the diversity of real-world scenarios from ground truth data. Diverse datasets enable models to generalize to a variety of domains, increasing their effectiveness in operating in different environments and use cases.

Sensor simulation can model all aspects of how autonomous machines perceive their environment without the need for real-world interaction. This includes the appearance, behavior, and content of any given scenario, as well as sensor noise, occlusions, adverse maneuvers from other agents, and challenging lighting and weather conditions. By using software-in-the-loop and hardware-in-the-loop testing in a safe, scalable environment, developers can thoroughly validate and refine their systems, ensuring improved safety and reliability before deployment.

Sensor simulation reduces the dependence on costly, large-scale data collection and annotation, as well as the number of physical prototypes required for testing and validation.

Sensor simulation allows developers to configure virtual worlds with automated pipelines for rapid iteration to improve performance and reduce end-to-end development time. Further, sensor simulation enables developers to prototype complete solutions before the availability of physical sensors.

Simulation of virtual sensors must mirror the physics and behavior of real-world sensors with a level of fidelity that gives developers the confidence to use simulated sensor data to augment and expand their existing real-world pipelines.

Extending and maintaining sensor simulation solutions to incorporate additional sensors or new functionalities—such as advanced rendering features or complex sensor behaviors like multi-bounce, multi-path ray-tracing effects for non-visual sensors—typically demands significant technical expertise.

Sensor simulation solutions that achieve the accuracy and scalability necessary for physical AI development can be costly to build and maintain. This often means standing up a large-scale infrastructure, and requires teams that can manage the entire software and hardware stack.

Sensor simulation solutions are often built with bespoke software packages on complex hardware and require specific domain expertise. There are no plug-and-play solutions that make it easy for developers to quickly connect their existing simulation workflows to sensor simulation and train/test their physical AI systems.

Effective sensor simulation incorporates the following components:

Developers can leverage a digital twin that includes 3D models of the operational environment. These environments may include vehicles, people, robots, factories, or streets, with material and visual properties. The digital twin should also include physical phenomena, such as how light interacts with objects. Such detailed representations allow sensor models to interact with their surroundings and extract ground truth labels for training and testing physical AI.

Within simulation environments, developers must define the physics-based behaviors of agents in 3D scenes, encompassing both light and matter. This includes accurately simulating actions such as a pedestrian walking, a box falling off a shelf, or the dynamics of a moving vehicle or robot. Each entity should adhere to the laws of physics, ensure authentic and realistic behavior, and generate accurate sensor output.

Sensor simulation models each step of a sensor pipeline to accurately emulate the physics and behavior of real-world sensors. This pipeline includes the behavior of light and radio waves, how transmitters and receivers in each sensor behave, and other internal workings specific to each sensor, like the spinning of a mechanical lidar or rolling shutter on a camera. Various physical phenomena such as multi-bounce, multi-path ray effects, Doppler, distortion of light by a lens, motion blur in image data, and low-light noise are all important aspects of reducing the domain gap between simulation and real data.

Sensor simulation can benefit any industry that uses autonomous machines or sensor-dependent devices.

Simulation is a foundational component of the autonomous vehicle development pipeline. Sensor simulation, in particular, is critical for testing and validating the perception and planning stack on high-fidelity, physically based sensor data.

Specifically, developers can replay real-world drives in sensor simulation. The ability to precisely repeat drives enables developers to benchmark performance, measure whether a stack is improving or regressing, and exhaustively test autonomous vehicles and driver assistance systems.

Sensor simulation also provides a proving ground to train an autonomous vehicle’s deep neural networks that power a vehicle’s perception. These networks can continuously experience new and diverse datasets to hone their ability to accurately understand their surroundings. Sensor simulation can also be used for open-loop data generation to create diverse datasets that can challenge an autonomous vehicle.

Finally, developers can perform closed-loop testing on a dynamic, reactive, and safety-critical platform. Closed-loop simulation with high-fidelity sensors operating at scale and high performance can help AV developers accelerate their ability to triage, debug, and develop new features. This helps developers validate autonomous driving systems for real-world deployment.

Robots that rely on perception can take full advantage of sensor simulation tools. 

Autonomous Mobile Robots (AMRs) have become common in factories and warehouses, where they are used to lift products and transport payloads. Equipped with numerous sensors, these AMRs, much like self-driving vehicles, must understand and safely navigate their surroundings.

Humanoid robots, which are designed to resemble and function like humans, depend on sensors to traverse diverse environments and interact with objects. Sensor simulation plays a crucial role in testing and enhancing the capabilities of these robots in unpredictable settings before they are commissioned in the real world.

Robotics developers use sensor simulation for pose estimations, which enables robotic manipulators or arms to grasp objects properly. Sensor simulation is also used to fine-tune AI models for optical inspection use cases, like detecting defects and sorting objects on assembly lines.

Sensor simulation can be used to train AI models powering smart spaces such as warehouses, airports, hospitals, and more.

Developers can train perception models on a range of applications, from worker safety, to path planning for autonomous mobile robots and fleet optimization, to inventory management. 

Sensor simulation can be used to validate the performance of various AI-driven robots and cameras in virtual representations of a physical place. This allows for scalable testing of different autonomous machines in a single, unified space.

Advanced medical devices rely on sensors for ultrasound and endoscopy as well as capturing and streaming medical data at the clinical edge. These devices require extremely high levels of accuracy and precision, especially when used in diagnostics or surgical procedures. Sensor simulation enables medical devices to be trained, tested, and calibrated to perform with surgical precision, reducing the risk of errors or misdiagnosis.

NVIDIA offers a suite of tools to support sensor simulation workflows.

NVIDIA Omniverse™ is a platform of APIs, SDKs, and services that enable developers to easily integrate Universal Scene Description (OpenUSD) and RTX rendering technologies into existing software tools and simulation workflows for building AI systems.

NVIDIA Omniverse Cloud Sensor RTX™ is a set of microservices that enable physically accurate sensor simulation to accelerate the development of fully autonomous machines of every kind. Built on the OpenUSD framework and powered by NVIDIA RTX™ ray-tracing and neural rendering technologies, Omniverse Cloud Sensor RTX accelerates the ability to generate high-fidelity sensor data for cameras, lidars, and radars used in autonomous machines to train and validate physical AI in a safe, repeatable way.