The commercial vehicle sector (especially trucks) has major role in economic growth of a nation. With improving infrastructure, increasing number of commercial vehicles and growing amount of Vulnerable Road Users (VRUs) on roads, accidents are also increasing. As per RASSI (Road Accident Sampling System India) FY2016-21 database, commercial vehicles are involved in 43% of total accidents on Indian roads. One of the major causes of these accidents is Driver Drowsiness and Inattention (DDI) (approx. 10% contribution in total accidents).
This paper describes novel driver-in-loop performance assessment methodology for comprehensive verification of Driver Monitoring System (DMS) for commercial vehicle application. Novelty lies in specification of test subjects, driving styles and variety of road traffic scenarios for verification of DMS system. Test setup is made modular to cater to different platform environments (Heavy, Intermediate, Light) with minor modifications.
The test setup development involved integration of three systems a) on-board cameras, b) modular driver-in-loop setup, and c) camera-based Driver Monitoring System; in unique way to validate the operational performance of the system. DMS system interfacing is ensured through vehicle's Controller Area Network (CAN) architecture, which provides accurate timestamps and duration measurements. The evaluation of the DMS was conducted under various conditions, including different Indian driver categories, facial structures, eyewear usage, skin complexions, and road scenarios.
DMS verification using this novel methodology could successfully predict driver drowsiness and inattention accurately and verified timely warnings. The detailed analysis also investigated the excellent correlation between the driver's actual state and the system's predictions. This innovative driver in loop assessment methodology enabled recommendations of critical improvements in proposed DMS system to enhance its operational performance.