Refining Object-Based Lidar Sensor Modeling — Challenging Ray Tracing as the Magic Bullet

Sensor and perception simulation is key for simulation based testing of automated driving functions. Depending on the testing use-case, different cause-effect chains for the specific sensor technology become relevant and the demanded computation times differ. In this work, a novel approach for object-based lidar simulation is introduced, identifying and modeling major sensor effects while balancing effect fidelity and computation time. With an explicitly designed static experiment, simple object based models are falsified, showing the need of a novel approach. Therefore, refined bounding boxes are designed and integrated into occlusion calculation. This new approach is compared to an advanced ray tracing simulation on an object output level. The comparison is conducted on the cause-effect chain of partial object occlusion, which has been identified as highly relevant. The modeling approach challenges ray tracing as the magic bullet for high-fidelity lidar front-end simulation. In direct comparison with the ray tracing approach, our novel approach stands out due to its significantly lower computation time. The newly developed object-based model is open source and publicly available at
https://gitlab.com/tuda-fzd/perception-sensor-modeling/object-based-generic-perception-object-model
.