The rapid development of the autonomous driving technology relies on the breakthroughs in several core technologies, pedestrian detection being a critical component which directly affects the safety and reliability of autonomous driving systems. Targeting the shortcomings inherent to the current pedestrian detection technologies, this paper proposes an optimization algorithm based on machine learning, which is intended to improve the pedestrian detection accuracy and real-time efficiency. In order to optimize the parameter configuration of deep learning models, this study validates the proposed approach on two public datasets. The obtained results demonstrate that the optimized model achieved significant improvements in both pedestrian detection accuracy and computational efficiency.
Autonomous Driving, Pedestrian Detection, Machine Learning, Optimization Algorithm.
Qing Rong TANG, Xiao Fang WANG, Yuan LIAO, "Optimization Method for Automated Pedestrian Detection in Autonomous Driving Based on Machine Learning", Studies in Informatics and Control, ISSN 1220-1766, vol. 34(2), pp. 89-96, 2025. https://doi.org/10.24846/v34i2y202508
All articles published by Studies in Informatics and Control (SIC) are published in accordance with the terms and conditions of the Creative Commons Attribution License (CC BY-NC-SA 4.0).