Applying Reservoir Computing for Driver Behavior Analysis and Traffic Flow Prediction in Intelligent Transportation Systems

In the realm of autonomous vehicles, ensuring safety through advanced anomaly detection is crucial. This thesis integrates Reservoir Computing with temporal-aware data analysis to enhance driver behavior assessment and traffic flow prediction. Our approach combines Reservoir Computing with autoencoder-based feature extraction to analyze driving metrics from vehicle sensors, capturing complex temporal patterns efficiently. Additionally, we extend our analysis to forecast traffic flow dynamics within road networks using the same framework. We evaluate our model using the PEMS-BAY and METRA-LA datasets, encompassing diverse traffic scenarios, along with a GPS dataset of 10,000 taxis, providing real-world driving dynamics. Through a support vector machine (SVM) algorithm, we categorize drivers based on their performance, offering insights for tailored anomaly detection strategies. This research advances anomaly detection for autonomous vehicles, promoting safer driving experiences and the evolution of vehicle safety technologies. By integrating Reservoir Computing with temporal-aware data analysis, this thesis contributes to both driver behavior assessment and traffic flow prediction, addressing critical aspects of autonomous vehicle systems.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Sethi, Sanchit

Chairs dc:contributor.committeechair

• Yi, Yang
• Walling, Jeffrey Sean

Committee member dc:contributor.committeemember

• Ball, Arthur Hugues

• echo-state networks
• reservoir computing
• intelligent transportation systems
• autonomous vehicles
• traffic flow prediction
• driver behavior classification
• trajectory prediction.

Statement dc:rights

• Creative Commons Attribution-ShareAlike 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-sa/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40996

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119315