Journal Article


Worsening perception: Real-time degradation of autonomous vehicle perception performance for simulation of adverse weather conditions

Abstract

Autonomous vehicles 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, development of robust autonomous vehicle 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 autonomous vehicle 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 compute 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.

Attached files

Authors

Fursa, Ivan
Fandi, Elias
Musat, Valentina
Culley, Jacob
Gil, Enric
Teeti, Izzeddin
Bilous, Louise
Sluis, Isaac Vander
Rast, Alexander
Bradley, Andrew

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2021
Date of RADAR deposit: 2021-11-04


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License


Related resources

This RADAR resource is Identical to [arXiv preprint] Worsening perception: Real-time degradation of autonomous vehicle perception performance for simulation of adverse weather conditions
This RADAR resource is the Accepted Manuscript of Worsening perception: Real-time degradation of autonomous vehicle perception performance for simulation of adverse weather conditions

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