Quantifying the impact of fog on autonomous driving object detectors and developing a fog-aware vehicle detector

An essential component of developing robust autonomous driving software is the ability to successfully navigate in various weather conditions, such as in fog, rain, and snow. Autonomous vehicles today rely on hardware (e.g., cameras) and sensors (e.g., lidar, radar) to understand their environment so they can navigate their surroundings accordingly. However, severe weather impacts the quality of data obtained by the hardware and sensors. For instance, in foggy weather conditions, the contrast in the images obtained by cameras drops significantly, making it difficult for intelligent image processing algorithms to perform object detection and image classification. In this project, we quantify the impact of fog on the accuracy and confidence levels of current state-of-the-art object detectors, focusing on the task of identifying other vehicles on the road in foggy weather conditions. We do so by curating a dataset of road images from the driver’s perspective, with various levels of fog synthetically added to each image. We also design a vehicle detector that can identify vehicles in fog with a higher accuracy and confidence level compared to current state-of-the-art detectors. Our fine-tuned detector’s persistence in correctly identifying vehicles is, on average, 4.69% higher in light fog, 13.38% higher in medium-intensity fog, and 23.65% higher in heavy fog.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Kore, Ruhi

Contributors dc:contributor

• Forsyth, David

• Fog
• Object
• Vehicle
• Detector
• Detectron
• Renderings

Statement dc:rights

• Copyright 2023 Ruhi Kore

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120450

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120450