Physically-Based Simulation of Automotive LiDAR
Not on the map. Why?
Abstract (arXiv)
We present an analytic model for simulating automotive time-of-flight (ToF) LiDAR that includes blooming, echo pulse width, and ambient light, along with steps to determine model parameters systematically through optical laboratory measurements. The model uses physically based rendering (PBR) in the near-infrared domain. It assumes single-bounce reflections and retroreflections over rasterized rendered images from shading or ray tracing, including light emitted from the sensor as well as stray light from other, non-correlated sources such as sunlight. Beams from the sensor and sensitivity of the receiving diodes are modeled with flexible beam steering patterns and with non-vanishing diameter. Different (all non-real time) computational approaches can be chosen based on system properties, computing capabilities, and desired output properties. Model parameters include system-specific properties, namely the physical spread of the LiDAR beam, combined with the sensitivity of the receiving diode; the intensity of the emitted light; the conversion between the intensity of reflected light and the echo pulse width; and scenario parameters such as environment lighting, positioning, and surface properties of the target(s) in the relevant infrared domain. System-specific properties of the model are determined from laboratory measurements of the photometric luminance on different target surfaces aligned with a goniometer at 0.01deg resolution, which marks the best available resolution for measuring the beam pattern. The approach is calibrated for and tested on two automotive LiDAR systems, the Valeo Scala Gen. 2 and the Blickfeld Cube 1. Both systems differ notably in their properties and available interfaces, but the relevant model parameters could be extracted successfully.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 5 Dec 2025.
Builds on
10 of 23 references.
References · 10
- 2016Playing for Data: Ground Truth from Computer Gamescited by 816
- 2020Virtual KITTI 2cited by 345
- 2016Virtual Worlds as Proxy for Multi-Object Tracking Analysiscited by 340
- 2016Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?cited by 194
- 2020LiDARsim: Realistic LiDAR Simulation by Leveraging the Real Worldcited by 137
- 2024LidarDM: Generative LiDAR Simulation in a Generated Worldcited by 53
- 2018Augmented LiDAR Simulator for Autonomous Drivingcited by 41
- 2022PCGen: Point Cloud Generator for LiDAR Simulationcited by 20
- 2020A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Trackingcited by 4
- 2024A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Projectcited by 2
Built on by
Not cited yet.