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Physics-Based Simulation Solutions for Testing Performance of Sensors and Perception Algorithm under Adverse Weather Conditions
Journal Article
12-05-04-0024
ISSN: 2574-0741, e-ISSN: 2574-075X
Sector:
Topic:
Citation:
Ozarkar, S., Gely, S., and Zhou, K., "Physics-Based Simulation Solutions for Testing Performance of Sensors and Perception Algorithm under Adverse Weather Conditions," SAE Intl. J CAV 5(4):297-312, 2022, https://doi.org/10.4271/12-05-04-0024.
Language:
English
Abstract:
Weather conditions such as rain, fog, snow, and dust can adversely impact sensing
and perception, limit operational envelopes, and compromise the safety and
reliability of advanced driver-assistance systems and autonomous vehicles.
Physical testing of an autonomous system in a weather laboratory and on-road is
costly and slow and exposes the system to only a limited set of weather
conditions. To overcome the limitations of physical testing, a physics-based
simulation workflow was developed by coupling computational fluid dynamics (CFD)
with optical simulations of camera and lidar sensors. The computational data of
various weather conditions can be rapidly generated by CFD and used to assess
the impact of weather conditions on the sensors and perception algorithms. The
developed CFD-optical workflow was tested using rainy conditions as a test case,
the data for which were generated using CFD and exported to optical simulation
software to assess how rainy conditions affect the performance of a visible
camera, lidar, and an open-source perception algorithm. The results of the
analysis indicate that the rainy conditions and intensification of the
conditions by other vehicles on the road degrade the performance of camera and
lidar sensors. The ability of the perception algorithm to detect vehicles
significantly deteriorated when rainy conditions changed to moderate rain.
Finally, a systematic perturbation of the rain data produced inconsistent
predictions from the perception algorithm, indicating the need to develop
weather-aware perception algorithms.