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Advanced Driver Assistance System(ADAS)

The Advanced Driver Assistance System, also known as ADAS (hereinafter referred to as ADAS) is an active safety technology that makes use of AI algorithms to conduct intelligent image analysis. With the help of sensors, ADAS collects first-hand data inside and outside the vehicle, identifies dynamic and static objects, and is capable of object detection and tracking. In this way, ADAS enables drivers to detect potential risks in the shortest time and helps prevent traffic accidents from occurring.


In the early stages, ADAS functioned by using passive alarms. As the science and technology advanced, ADAS is now able to take preventative precautions, thus further preserving driver safety. In order to lower the accident rate, ADAS technology may be a standard implementation for vehicles in the near future.


After years of exploration and investment, Streamax has made remarkable achievements in ADAS technology, and supports a comprehensive suite of ADAS functions: forward collision warning, headway monitoring warning, urban forward collision warning, lane departure warning and pedestrian collision warning. Most functions mentioned above are unique to Streamax.


1.Forward Collision Warning

The forward collision warning (also known as FCW) system determines potential risks by sensing and calculating the relative headway distance between the user vehicle and the vehicle ahead, as well as calculating the relative speed of the vehicle when it is driving at a higher speed. Forward collision warnings are given once the potential collision risk is detected.


2.Headway Monitoring Warning

The headway monitoring warning (also known as HMW) system monitors headway time by sensing and calculating the headway distance between the user vehicle and the vehicle ahead, as well as calculating the absolute speed of the vehicle in real time. When the headway time (the unit is in seconds) is less than the threshold alarm time configured in the system, an alarm will be immediately generated for the driver, reminding him to maintain a safe following distance and ultimately avoid a rear-end collision.


3.Urban Forward Collision Warning

The urban forward collision warning (also known as UFCW) system predicts possible low-speed collisions by calculating the headway distance between the user vehicle and the vehicle ahead, as well as by calculating the speed in real time when the vehicle is driving at a low speed (UFCW usually works when the speed ranges from 0 to 30 km/h). An alarm is immediately generated once an imminent low-speed collision is detected.

4.Lane Departure Warning

The lane departure warning (also known as LDW) system senses road conditions ahead via the ADAS algorithm, then determines whether or not there is a lane departure and promptly sends a warning to the driver. The lane departure warning is given to the driver when he departs from the original lane unconsciously (without using the turn signal), thereby preventing possible traffic accidents resulting from sudden lane departure.


5.Pedestrian Collision Warning

The pedestrian collision warning (also known as PCW) system detects pedestrians and riders in front of the user vehicle. The system makes comprehensive decisions according to vehicle speed and the distance between the vehicle and detected pedestrians, then provides an alarm to drivers immediately if there is a high potential for a crash, thereby preventing possible pedestrian collisions.


Streamax has two core competitive edges in ADAS technology thanks to years of experience: independent intellectual property rights and a database of abundant video footage. We have enriched ADAS functions and obtained several patents for innovation through independent research and development, basically mastering all ADAS key technologies based on image identification.


At the same time, Streamax is strongly capable of acquiring vehicle data. It also conducts highly targeted R&D tests for analyzing driving habits and complex road conditions.Streamax has established a database of road conditions and objects by collecting a vast amount of data in various weather and lighting conditions. We have also acquired data of millions of images for manual annotation, training the algorithm to become more accurate and user-friendly, and bolstering the effectiveness and convenience of the technology.


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