Faculty Advisor or Committee Member

Alexander Wyglinski, Advisor

Identifier

etd-3026

Abstract

The ability to look outward from your vehicle and assess dangerous peer behavior is typically a trivial task for humans, but not always. Distracted driving is an issue that has been seen on our roadways ever since cars have been invented, but even more so after the wide spread use of cell phones. This thesis introduces a new system for monitoring the surrounding vehicles with outside facing cameras that detect in real time if the vehicle being followed is engaging in distracted behavior. This system uses techniques from image processing, signal processing, and machine learning. It’s ability to pick out drivers with dangerous behavior is shown to be accurate with a hit count of 87.5%, and with few false positives. It aims to help make either the human driver or the machine driver more aware and assist with better decision making.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Electrical & Computer Engineering

Project Type

Thesis

Date Accepted

2019-12-03

Accessibility

Unrestricted

Subjects

Distracted Driving, Anomaly detection, Machine learning, Signal Processing, Vehicles

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