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.

Creator

• Radlbeck, Andrew J

Colaboradores

• Huang, Xinming
• Wyglinski, Alexander
• Brown, Donald R.

Degree

• MS

Unit

• Electrical & Computer Engineering

Publisher

• Worcester Polytechnic Institute

Identifier

• etd-3026

Palabra Clave

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

Advisor

• Huang, Xinming
• Wyglinski, Alexander
• Brown, Donald R.

Orcid

• https://orcid.org/0000-0001-7231-3776

Defense date

• 2019-12-03

Year

• 2019

Date created

• 2019-11-29

Resource type

• Thesis

Rights statement

• In Copyright

Última modificación

• 2021-01-05