When people think about high-fidelity radar simulation, they typically imagine one thing: a detailed 3D environment. Geometry, meshes, materials, everything needs to be modeled, prepared, and maintained before a single ray can be traced. But what if you could get the same physical fidelity without requiring a predefined 3D scene at all?

When we talk about lidar simulation, most discussions focus on sensor models, ray tracing, or point cloud generation. But in many real-world applications, the biggest challenge lies elsewhere: the materials.

Simulating lidar sounds straightforward at first: send out light, measure the distance, and construct the point cloud. In practice, however, the challenge lies in what happens in between. The way infrared light interacts with the world is anything but simple, and without modeling that interaction correctly, even the best sensor model will fall short.

We apply a development method that embeds continuous verification & validation (V&V) into every iteration of our perception sensor model development, so lidar and radar models become more credible, traceable, and automation‑ready as they evolve.

The automotive and mobility industry is embracing virtual testing at scale. To make these simulations realistic and interoperable, standardization is key. That’s why we created the Standardized Co-Simulation Demo , an open-source example of how to combine scenario execution, sensor simulation, and multi-domain orchestration using OpenMCx , esmini , and our own physical sensor models , all aligned with ASAM OpenX standards . Check out the GitHub repository here . Co-simulatio

The DIN SAE SPEC 91471 offers a structured way to assess lidar performance. In this blog post, we will explore key performance tests, each critical for understanding lidar capabilities.

In this video, Clemens, our CTO, introduces the Persival Weather Test Bench. It automatically records sensor measurements in adverse...

In this video, Clemens, our CTO, demonstrates how to simulate radial velocity measurements using the Sensor Model Development Library...

In this video, Clemens, our CTO, explores how to simulate timing effects in perception sensors using the Sensor Model Development Library...

In this video, Clemens, our CTO, demonstrates how to build a physical perception sensor model using standardized interfaces like ASAM OSI...

In this video, Clemens, CTO of Persival, demonstrates how to map physical material properties to 3D geometries using the ASAM...

In this video, Clemens, CTO of Persival, shifts focus from geometry to the material side of the ASAM OpenMATERIAL 3D standard. He...

In this video, Clemens, CTO of Persival, dives into how animations are handled using standardized formats and interfaces, specifically...

In this video, Clemens, CTO of Persival, demonstrates how ASAM OpenMATERIAL 3D works in combination with ASAM OpenSCENARIO and the ASAM...

In this video, Clemens, CTO of Persival, introduces the ASAM OpenMATERIAL 3D standard, focusing on how to standardize the geometry of 3D...

In this video, Clemens, CTO of Persival, provides a short introduction to ASAM OSI trace files. Building on the previous tutorials,...

In this video, Clemens, CTO of Persival, explains how to effectively transfer data between individual models in a Co-Simulation using the...

In this video, Clemens, CTO of Persival, demonstrates how to create ASAM OpenDRIVE and ASAM OpenSCENARIO files using an open-source tool....

In this video, Clemens, CTO of Persival, introduces the main ASAM OpenX standards for automotive simulation. These standards help ensure...

In this video, Clemens, CTO of Persival, introduces the concept of Co-Simulation and its benefits. Unlike all-in-one simulation tools,...