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Worsening Perception: Real-Time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions
- Ivan Fursa - Oxford Brookes University, UK ,
- Elias Fandi - Oxford Brookes University, UK ,
- Valentina Musat - Oxford University, UK ,
- Jacob Culley - Oxford Brookes University, UK ,
- Enric Gil - Oxford Brookes University, UK ,
- Izzeddin Teeti - Oxford Brookes University, UK ,
- Louise Bilous - Oxford Brookes University, UK ,
- Isaac Vander Sluis - StreetDrone Limited, UK ,
- Alexander Rast - Oxford Brookes University, UK ,
- Andrew Bradley - Oxford Brookes University, UK
Journal Article
12-05-01-0008
ISSN: 2574-0741, e-ISSN: 2574-075X
Sector:
Topic:
Citation:
Fursa, I., Fandi, E., Musat, V., Culley, J. et al., "Worsening Perception: Real-Time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions," SAE Intl. J CAV 5(1):87-100, 2022, https://doi.org/10.4271/12-05-01-0008.
Language:
English
Abstract:
Autonomous vehicles (AVs) rely heavily upon their perception subsystems to “see”
the environment in which they operate. Unfortunately, the effect of variable
weather conditions presents a significant challenge to object detection
algorithms, and thus, it is imperative to test the vehicle extensively in all
conditions which it may experience. However, the development of robust AV
subsystems requires repeatable, controlled testing—while real weather is
unpredictable and cannot be scheduled. Real-world testing in adverse conditions
is an expensive and time-consuming task, often requiring access to specialist
facilities. Simulation is commonly relied upon as a substitute, with
increasingly visually realistic representations of the real world being
developed. In the context of the complete AV control pipeline, subsystems
downstream of perception need to be tested with accurate recreations of the
perception system output, rather than focusing on subjective visual realism of
the input—whether in simulation or the real world. This study develops the
untapped potential of a lightweight weather augmentation method in an autonomous
racing vehicle—focusing not on visual accuracy but rather the effect upon
perception subsystem performance in real time. With minimal adjustment, the
prototype developed in this study can replicate the effects of water droplets on
the camera lens and fading light conditions. This approach introduces a latency
of less than 8 ms using computer hardware well suited to being carried in the
vehicle—rendering it ideal for real-time implementation that can be run during
experiments in simulation and augmented reality testing in the real world.