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The "Alami FPE-900" is an AI driver monitoring system designed to enhance safety in bus operations that carry passengers. It analyzes eye and facial movements using infrared cameras to detect dangerous behaviors such as drowsiness, distraction, phone calls, and smoking in real-time. Warnings can be communicated through audio and, with an optional vibration cushion, can notify passengers without causing discomfort. Video and data can be recorded, aiding safety training by operation managers. 【Features】 - Instant detection of drowsiness and distraction using AI - Safe and quiet alerts with audio and vibration warnings - Reliable recognition even at night or in tunnels - Supports management tasks with video recording and digital tachograph integration - Gains trust and evaluation through improved safety quality *For more details, please refer to the related links or feel free to contact us.

- Equipped with a high-precision AI facial recognition engine specialized for the rapidly changing in-vehicle environment. By using an active infrared method that does not rely on visible light, stable tracking is achieved even under conditions that are difficult for conventional cameras. ■ Technical Points - IR Sensing: Using infrared LEDs in the 850nm band and a dedicated filter, the main facial features can be accurately plotted even in direct sunlight or complete darkness. - Obstruction Handling: Detection logic that is not affected by the driver's attire, such as dark sunglasses, prescription glasses, wearing a mask, or wearing a hat at night. - Posture Correction: Analyzes the tilt of the head and the direction of gaze in 3D. Complex behaviors such as looking sideways or downward can also be accurately distinguished.

"Alarmee" is an analytical device equipped with a unique AI algorithm. Its greatest feature is that it is "easy to integrate with existing onboard devices and management systems." There is no need to rebuild the system from scratch. - Digital Tachograph Integration (RS232C): It outputs detection data for drowsiness and distraction as a serial output. The "AI judgment results" can be fed into existing operation management systems, enabling immediate centralized management. - Dashcam Linkage (I/O Output): It uses detection signals as triggers for external transmission. This allows for the direct utilization of the event recording function on the dashcam, reducing the labor required for video verification. ■ Technical Points (Simplification of Integration) - Open Specifications: Pin assignments and communication protocols are published, significantly reducing the development workload during consideration. - Equipped with Universal Terminals: With RS232C and I/O output, it can connect directly to devices from various manufacturers, regardless of age. - Edge Completion: Analysis is completed within the terminal. There is no need for complex communication control design like that required for cloud integration.

"Alarmee" offers the following strengths in terms of speed: - There is no waiting for communication because analysis and judgment are performed within the device without sending images to the cloud. - With a high-speed response that is not affected by communication delays, warnings and external interactions can be executed immediately after detection. * It is easy to integrate into existing in-vehicle devices, contributing to a reduction in accident risk. - A key feature is the ability to stabilize response speed even in environments with unstable signals, such as mountainous areas and tunnels.

Special transportation of hazardous materials and chemicals, unlike general logistics, is directly linked to serious accidents such as explosions, fires, and environmental pollution from a single incident, with damage scales that are vastly different. Relying solely on the individual attention of drivers to prevent accidents in this harsh environment poses a significant risk. Alarmy continuously monitors driver conditions with AI, detecting real-time warning signs such as closing eyes, yawning, and distracted driving, and provides immediate alerts. As a "second passenger seat" that helps prevent major accidents before they occur, it reduces the burden and risks for drivers.

In the field of home delivery and route delivery (last mile/multiple deliveries), operations often involve making several deliveries in a day, and even when returning to the depot, the time until the next delivery is short, making it difficult to secure installation time for safety measures, which tend to be postponed. Additionally, during busy periods, there is a mix of additional vehicles, support vehicles, and leased vehicles, making it challenging to maintain uniform safety measures across all vehicles. Alarmy is an AI driver fatigue monitoring system that can be retrofitted to existing vehicles. It operates on power supplied from sources like cigarette lighter sockets, so no modifications to the vehicle are necessary. It can be installed quickly, allowing for implementation during inspection or waiting times, enabling safety measures to be initiated without halting daily operations. Testing can be conducted with just one unit, allowing for a gradual expansion after confirming operations and increasing the number of bases and vehicles. This can achieve a reduction in accident risk without increasing the burden on the field.

Alarmee is a "driver fatigue monitoring system" that detects the driver's eye and head movements using infrared sensors and cameras, and analyzes and warns of signs of fatigue and dangerous behavior with AI algorithms. It is designed to function even when wearing glasses or sunglasses, thanks to sensor technology that captures detailed facial images in dark environments and corrects for visual impairments such as reflections (note: recognition rates may decrease depending on the type of lens). When an anomaly is detected, it alerts the driver with an alarm and can also be integrated with existing devices such as digital tachographs and dash cameras.

Alarmee is a driver fatigue monitoring system designed to address the ongoing challenge of "costs arising from accidents" in the logistics and trucking industry. It uses AI, infrared sensors, and cameras to detect signs of fatigue and drowsiness, capturing dangerous states such as dozing off and distraction at an early stage. When danger is detected, it immediately alerts the driver, allowing for intervention before an accident occurs. As a result, it reduces the likelihood of accidents and makes it easier to manage not only unexpected expenses but also the burden of insurance costs that continue in subsequent years. Being an aftermarket solution, it can be easily installed and utilized as a swift accident prevention measure.

The driver monitoring device "Alarmi" detects increasing instances of "drowsiness" and "distraction" during night shifts and long hours of operation in taxis and hired vehicles using AI, and immediately alerts the driver with an alarm. The detection targets include "drowsiness," "yawning," "looking away," and "driving while distracted." It captures the driver's condition day and night using infrared sensors and cameras, and is designed to be easy to use even when wearing glasses, sunglasses, or masks. Photos taken when the alarm is activated can be saved with date and time on an SD card, which can also be used for review and guidance. Additionally, it supports integration with dash cams and operational management devices through I/O, RS232, and video output, thereby systematizing safety measures on-site.

Safety guidance for taxis and hired vehicles tends to rely on "the experience of the person in charge," "different standards at each base," and "lack of records leading to failure in preventing recurrence." Alarmy supports the visualization and standardization of safe operation management and guidance with a design that can be used in combination with drive recorders and operation management. It can connect with your existing digital tachographs, drive recorders, MDVRs, etc., allowing alarm signals, images, and records to be integrated into the existing management flow. Photos taken when an alarm is triggered can be saved with date and time on an SD card, providing clear evidence for guidance. This transforms guidance into a "sustainable system" without increasing the burden on the field.