One-Stage Brake Light Status Detection Based on YOLOv8.

Despite the advancement of advanced driver assistance systems (ADAS) and autonomous driving systems, surpassing the threshold of level 3 of driving automation remains a challenging task. Level 3 of driving automation requires assuming full responsibility for the vehicle's actions, necessitating the acquisition of safer and more interpretable cues. To approach level 3, we propose a novel method for detecting driving vehicles and their brake light status, which is a crucial visual cue relied upon by human drivers.

Deep Learning-Based Driver's Hands on/off Prediction System Using In-Vehicle Data.

Driver's hands on/off detection is very important in current autonomous vehicles for safety. Several studies have been conducted to create a precise algorithm. Although many studies have proposed various approaches, they have some limitations, such as robustness and reliability.

Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving.

As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving.

DRER: Deep Learning-Based Driver's Real Emotion Recognizer.

In intelligent vehicles, it is essential to monitor the driver's condition; however, recognizing the driver's emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver's emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces.