Analyzing and Mitigating Bias for Vulnerable Road Users by Addressing Class Imbalance in Datasets

Analyzing and Mitigating Bias for Vulnerable Road Users by Addressing Class Imbalance in Datasets

Vulnerable road users (VRUs), including pedestrians, cyclists, and motorcyclists, account for approximately 50% of road traffic fatalities globally, as per the World Health Organization. In these scenarios, the accuracy and fairness of perception applications used in autonomous driving become critic...

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Bibliographic Details
Main Authors: Dewant Katare, David Solans Noguero, Souneil Park, Nicolas Kourtellis, Marijn Janssen, Aaron Yi Ding
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of Intelligent Transportation Systems
Subjects:
Behaviour metrics
class imbalance
data disparities
cost-sensitive learning
sample representation
object detection
Online Access:https://ieeexplore.ieee.org/document/10977047/
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https://ieeexplore.ieee.org/document/10977047/

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