- Many commercial off-the-shelf (COTS) to connect common camera types
- Integration of DMS-specific camera sensors like NIR (Near InfraRed)
- Start developing your applications now to meet DMS requirements for NCAP (2023) and new approval of trucks and buses as of 2024
- Easily include Python-based AI algorithms for more robust solutions
- Bring your DMS system to the market faster using one framework designed to develop and test multi-modal sensor applications
The Task
Driver status monitoring (DSM) will be an integral part of the European New Car Assessment Programme (Euro NCAP) for passenger cars as of 2023. The DSM systems will be expected to detect driver distraction, fatigue, and unresponsiveness. From 2024 on, driver drowsiness and attention warning will be required for the approval of new trucks and buses, as will be the EU Advanced Driver Distraction Warning for type approval. For automated driving level 3 and higher, the importance of driver monitoring use cases will increase, with even more cameras and 60 GHz radar sensors installed in the cabin.
The Challenge
To detect the driver status, a DSM application can evaluate the output of different vehicle functions, such as lane keeping, steering movement analysis, or turn signal activation. However, the most valued data is provided by in-cabin cameras placed in the front of vehicle that observe the driver. To overcome adverse lighting conditions, specific NIR or NIR+NGR camera types are often used. A key issue is to address the robustness of the detecting application. Various conditions have to be considered when testing the quality of detections. For example, age and sex of the driver, eyewear, facial hair and occlusion, as well as the driver’s behavior such as eating, talking, or laughing. A promising approach to overcoming these difficulties is the utilization of deep learning algorithms and specific training data.
