Comprehnsive Measurement and Evidential Evaluation of Driver Drowsiness and Alertness Warning System

<div class="section abstract"><div class="htmlview paragraph">The paper aimed to improve the accurate quantification of driver drowsiness and to provide comprehensive, evidence-based validation for a Vision-Based Driver Drowsiness and Alertness Warning System. Advanced quantification of driver drowsiness is designed to enhance distinction of true positive events from False Positive and False Negative events. Methodology to pursue this included assessing inputs such as facial features, driver visibility, dynamic driving tasks, driving patterns, driving course time and vehicle speed. The system is programmed to actively learn Eye Aspect Ratio (EAR) reference and adapt personalised EAR threshold value to process EAR frames against the learnt threshold value. This method optimized the data frames to enhance the evaluation and processing of essential frames, thereby reducing delays in the processor and the Human-Machine Interface (HMI) warning module. Comprehensive validation is systematically conducted within a controlled test track environment to ensure precise execution of protocols, maintaining inputs closely aligned with real-time scenarios. The test methodology comprised the execution of pre-defined protocols that is steering robot and a technology-neutral procedure. Pre-defined protocols are scenarios created using the aforementioned assessing inputs. Cartesian coordinates of the system’s camera and driver eye point relative to the seating reference point (SgRP) are identified using a coordinate measurement machine (CMM) to measure the driver's position within the camera's field of view and mark the visibility zones. The protocols are executed with precision using a global navigation satellite system (GNSS), visual sensor, audio sensor and data logger. Subsequently, the system is tested with number of drivers trained on the Karolinska Sleepiness Scale (KSS) to conduct technology-neutral method for statistical analysis. Detailed analysis of the tested data, concluded with results and explored future prospects for quantifying driver drowsiness are discussed. The paper also discussed observations and challenges associated with the functionality of conventional systems and protocols currently deployed in the market.</div></div>