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Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities
Stefan Muckenhuber1, Hannes Holzer1, Zrinka Bockaj2
1Virtual Vehicle Research GmbH, Inffeldgasse 21A, 8010 Graz, Austria.
Sensors (Basel, Switzerland)
|June 14, 2020
Summary
This study introduces a novel lidar sensor model for autonomous vehicles, enhancing virtual testing by accurately simulating material properties like angle-dependent reflectance. This improves the realism of environment perception systems.
Area of Science:
- Robotics and Artificial Intelligence
- Sensor Technology
- Materials Science
Background:
- Automated driving systems require robust environment perception, often validated through simulations.
- Accurately modeling sensor behavior, particularly lidar, is crucial for realistic virtual testing.
- Representing diverse material properties poses a significant challenge for current sensor models.
Purpose of the Study:
- To develop a new lidar sensor modeling approach for automated driving.
- To incorporate material properties, specifically angle-dependent reflectance, into lidar models.
- To enhance the realism of virtual test environments for perception system validation.
Main Methods:
- Introduced a novel lidar modeling approach considering material reflectance and sensor detection range.
- Developed a new measurement device using a time-of-flight camera to measure infrared reflectance.
- Created a new 7-class, 23-subclass material classification for automotive lidar modeling.
- Calibrated the measurement device with Lambertian targets and validated with NASA ECOSTRESS data.
- Conducted lidar measurement campaigns with a prototype and data from 12 common lidar types.
Main Results:
- Presented angle-dependent reflectance measurements for 9 material subclasses.
- Demonstrated the capability of the new measurement device using real-world spectral data.
- Parametrized lidar capabilities based on material properties and sensor performance data.
- Provided a comprehensive dataset for improving lidar sensor models in automotive applications.
Conclusions:
- The proposed lidar modeling approach realistically integrates material properties, advancing virtual testing for autonomous driving.
- The developed measurement device and material classification provide a foundation for more accurate sensor simulations.
- This work contributes to the development of reliable perception systems for future automated vehicles.
