What We Do / Automobile Safety
Automobile Safety
Overview
This software leverages proprietary deep learning (AI) technology to significantly improve vehicle driver safety, which has traditionally been challenging. Our custom model, enhanced with training, can detect behaviors such as drowsiness or mobile phone usage while driving, enabling the development of safety systems in a short time and at low cost.
"The Problem with 2024" in Japan
-34 %
The Ministry of Land, Infrastructure, Transport and Tourism estimates that by 2030, Japan will face a shortage in transportation capacity by 34%. This is equivalent to approximately 900 million tons in weight.
960 h
As part of Japan's work-style reform, from April 2024, stricter regulations on overtime limit annual overtime hours to 960h.
21.8 %
Workers aged 60 and older now make up 21.8% of the total employed population, a historical high.
Automatically detecting a danger of drivers
Driver distraction is a major cause of road accidents, and among the most common forms of distraction are mobile phone use and smoking while driving. Both activities require the driver's attention to be diverted away from the road, increasing the likelihood of errors, slower reaction times, and accidents. According to studies, drivers using their phones are four times more likely to be involved in a crash, while smoking not only occupies one hand but also diverts attention during lighting, smoking, or disposing of cigarettes.
In response to the growing concern over distracted driving, advances in computer vision and artificial intelligence (AI) have paved the way for systems that can automatically detect and alert drivers when engaging in these dangerous behaviors. Such systems leverage in-vehicle cameras and AI algorithms to monitor the driver's hand movements, head position, and facial expressions, recognizing when a driver is holding a phone or cigarette. By analyzing visual cues, these detection systems can warn drivers in real-time, helping reduce the risks associated with distraction.
Automatically detecting drowsiness
drowsy (1).mp4Road safety is a global concern, with driver drowsiness recognized as a significant factor contributing to accidents and fatalities. According to the World Health Organization (WHO), drowsy driving accounts for a considerable portion of traffic-related incidents worldwide. As drivers become fatigued, their reaction times slow, decision-making deteriorates, and the risk of collisions increases. Traditional preventive measures, such as educational campaigns and regulations limiting driving hours, while effective to some extent, cannot adequately address the real-time detection and intervention needed to prevent drowsy driving accidents. We have enabled the development of more sophisticated systems for detecting driver drowsiness. These technologies have the potential to monitor driver behavior in real-time, recognizing signs of fatigue based on visual cues such as blinking patterns, yawning frequency, or head position.