Vision-Based Road Rage Detection Framework in Automotive Safety Applications.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Transportation Safety

Background:

• Road rage is a significant factor in global road accidents, leading to numerous fatalities and injuries annually.
• Existing driver monitoring systems often struggle with varying facial orientations and lighting conditions.
• There is a critical need for robust systems to monitor driver concentration and emotional states to prevent accidents.

Purpose of the Study:

• To develop an Advanced Driver Assistance System (ADAS) module for minimizing road accidents caused by driver road rage.
• To create a system capable of detecting driver rage through facial expression analysis, independent of face orientation and cabin lighting.
• To enhance transportation safety by providing timely alerts to drivers exhibiting signs of road rage.

Main Methods:

• Integration of face detection and facial expression classification algorithms designed to handle non-ideal conditions (multi-pose, varying light).
• A decision-making strategy based on the temporal consistency of classified facial expressions (anger, disgust) to estimate road rage.
• Performance evaluation using real-world driving data and three standard benchmark datasets, including those with non-frontal facial expressions.

Main Results:

• The proposed module demonstrates competence in estimating road rage through facial expression recognition under multi-pose and changing lighting conditions.
• The system achieves state-of-the-art recognition rates on selected benchmark datasets.
• Experimental results validate the system's effectiveness in real-world driving scenarios.

Conclusions:

• The developed ADAS module is effective for road rage estimation using facial expression recognition, even in challenging environmental conditions.
• The system contributes to increased transportation safety by proactively addressing driver rage.
• The algorithmic pipeline offers a robust solution for real-time driver monitoring and intervention.