The Key to Credible Sensor Simulation: From Scenarios to Validation Evidence

As virtual testing becomes increasingly important, one question is being asked across the industry: How can we determine whether a sensor simulation is credible enough for its intended use case?

Many teams approach this challenge from the perspective of the sensor model itself. They discuss model fidelity, physical accuracy, or the number of effects that should be included.

We believe the starting point should be different: Credible simulation begins with understanding which physical effects actually influence the scenarios that matter.

Start with Scenarios, Not Models

Every sensor model is built for a purpose. An ADAS function must handle specific Euro NCAP scenarios. An autonomous drone faces different environmental conditions than an automated agricultural machine or a railway perception system.

The first step is therefore identifying the relevant scenarios and determining which sensor effects influence their outcome.

For example, some scenarios heavily depend on detection range and sensitivity, while others are influenced by angular accuracy, resolution, weather effects, or material characteristics.

Mapping and ranking radar effects by scenarios to derive the sensor effect relevance

By mapping scenarios to sensor effects, their relevance can be ranked and prioritized. Effects can be classified as critical, relevant, or negligible for the target application.

This immediately creates transparency about where development and validation efforts should be focused.

Validate the Effects That Matter

Once the relevant effects are known, validation becomes much more structured. Instead of validating an entire sensor model as a black box, individual effects can be compared against measurements and reference data. Detection performance, weather influence, material behavior, propagation effects, and other relevant characteristics can be assessed independently.

This approach creates a direct connection between requirements, implementation, and evidence. Equally important, it allows simulation teams to avoid spending effort on effects that have little impact on the target scenarios while focusing on those that determine system behavior.

From Effect Validation Back to Scenario Coverage

The real objective is understanding which scenarios can be simulated credibly based on the available validation evidence.

Validation of individual effects is not the final goal.

Sensor effect map showing the covered credible scenarios after effect-wise model validation

By mapping validated effects back to the scenarios that depend on them, it becomes possible to determine where sufficient credibility has been achieved and where additional validation is required.

The result is a transparent chain connecting requirements, effects, measurements, validation results, and scenario coverage.

Rather than claiming that a model is generally realistic, this approach provides evidence for the specific scenarios the model is intended to represent.

Credibility Requires Continuous Validation

Validation is often treated as a final project milestone. In reality, it is a continuous process.

New measurement data becomes available. Sensor models evolve. Additional effects are integrated. Customers deploy models on new platforms and architectures. Every change potentially impacts the credibility argument.

Continuous validation with Avelon

For this reason, we believe that sensor simulation requires continuous validation. Measurement data, re-simulation, automated evaluation, and reporting should become part of an ongoing workflow rather than a one-time exercise.

At Persival, this includes dedicated sensor performance measurements, automated evaluation pipelines, and continuous validation workflows that allow models to be re-assessed whenever new data or model updates become available. This not only improves credibility but also significantly increases efficiency.

The Key to Credible Sensor Simulation

Sensor simulation does not become credible because every imaginable physical effect is modeled. It becomes credible when relevant scenarios are identified, the underlying sensor effects are understood, those effects are validated against measurements, and the resulting evidence can be traced back to scenario coverage.

This is the workflow we follow at Persival.

And it is why we believe that credible sensor simulation is not a future vision.

The necessary building blocks already exist. The key is applying them in a systematic and continuously validated process.

It is achievable today.