Reliable perception is one of the fundamental requirements for automated driving systems on-road and especially off-road. While lidar, radar, and camera sensors perform remarkably well under clear weather conditions, their behavior can change significantly when visibility deteriorates.

What is the key to credible sensor simulation? At Persival, we believe the answer is not more complexity, but more traceability. By connecting scenarios, sensor effects, measurements, and validation evidence, simulation credibility becomes a structured engineering process rather than a matter of belief.

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 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...