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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

A Navigation Probability Map in Pedestrian Dynamic Environment Based on Influencer Recognition Model.

Zhi Qiao¹, Lijun Zhao¹, Xinkai Jiang¹

¹School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|December 30, 2020

Summary

This summary is machine-generated.

This study introduces a navigation probability map using the influencer recognition model (IRM) to improve robot navigation safety in crowded areas. The method helps robots predict pedestrian movement, reducing collision risks in dynamic environments.

Keywords:

pedestrian pattern social navigation trajectory analysis

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Robot navigation in dynamic environments with pedestrian traffic presents significant safety and efficiency challenges.
Pedestrian unpredictability, especially rapid movements, complicates collision avoidance for robots.

Purpose of the Study:

To propose a novel robot navigation method addressing challenges posed by rapid pedestrian movement.
To enhance robot safety and efficiency in dynamic environments like train stations.

Main Methods:

Developed a navigation probability map leveraging the influencer recognition model (IRM).
The IRM infers pedestrian turning causes from trajectory data to create the map.
Integrated the navigation probability map with the artificial potential field (APF) method for robot path planning.

Main Results:

The proposed method effectively analyzes changes in pedestrian trajectories to generate a navigation probability map.
Demonstrated the effectiveness of the combined navigation probability map and APF method on a custom dataset.
Successfully addressed the collision problem caused by rapid pedestrian movements.

Conclusions:

The navigation probability map, based on the influencer recognition model, offers a viable solution for robot navigation in pedestrian-rich environments.
This approach enhances robot's ability to navigate safely and efficiently amidst dynamic pedestrian flows.
The study validates the proposed method using a unique dataset of pedestrian movements.