Development and evaluation of wind tunnel testing methodology for ADAS camera perception in rain

Advanced Driver Assistance System (ADAS) technologies are rapidly improving to enhance road safety and reduce accidents. However, adverse weather, particularly rain, continues to degrade sensor perception and effectiveness. Despite this, few studies address sensor degradation due to rain, with no established standards for benchmarking sensor performance loss. The objective of this thesis is to develop a methodology that surpasses conventional spray-based approaches in realism, allowing for controlled, repeatable, and quantifiable evaluation of sensor performance in rain. This thesis develops VeRSA, the most realistic indoor rain simulation system in open literature, now adopted commercially. Using VeRSA, camera image quality and object detection under dynamic rain are benchmarked, revealing key limitations in existing metrics. These findings enable the creation of rain-degraded datasets to enhance detection by retraining neural networks. Finally, a novel mathematical model is derived and validated to correlate rainfall with image degradation, establishing a foundation for predicting perception degradation.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Li, Long

Advisor dc:contributor.advisor

• Agelin-Chaab, Martin

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/2010

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/2010