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Updated: Dec 16, 2025

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios.

Jingwei Cao¹, Chuanxue Song¹, Silun Peng^1,2

¹State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China.

Sensors (Basel, Switzerland)

|July 3, 2020

Summary

This summary is machine-generated.

This study enhances pedestrian detection for intelligent vehicles using an improved YOLOv3 algorithm. The new method achieves high accuracy and real-time performance in complex environments, boosting road safety.

Keywords:

YOLOv3 convolutional neural network driving assistance intelligent vehicle pedestrian detection

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Last Updated: Dec 16, 2025

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Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

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Published on: June 9, 2020

Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Traditional pedestrian detection struggles with environmental factors and real-time accuracy for intelligent vehicles.
Deep learning object detection, like YOLOv3, shows promise but has limitations for pedestrian detection.

Purpose of the Study:

To propose an improved pedestrian detection algorithm for intelligent vehicles operating in complex scenarios.
To enhance the accuracy and real-time performance of pedestrian detection.

Main Methods:

Analyzed limitations of the original YOLOv3 algorithm for pedestrian detection.
Modified YOLOv3 by adjusting grid cell size, using improved k-means clustering, enhancing multi-scale bounding box prediction, and incorporating Soft-NMS.
Conducted experiments on INRIA Person and PASCAL VOC 2012 datasets.

Main Results:

Achieved a mean Average Precision (mAP) of 90.42%.
Attained an average processing time of 9.6 ms per frame.
Demonstrated superior accuracy, real-time performance, robustness, and generalization compared to other algorithms.

Conclusions:

The improved YOLOv3 algorithm significantly enhances pedestrian detection accuracy and speed in complex scenarios.
The algorithm offers robustness, anti-interference capabilities, and high network stability for intelligent vehicles.
Improvements contribute to pedestrian road safety and the advancement of intelligent vehicle technology.