Umpaipant, Wachirawit2024-07-032024-07-032024-07-032024-06-21http://hdl.handle.net/10012/20693This thesis investigates the effectiveness of dynamic alert systems tailored to drivers' cognitive states in automated driving environments, focusing on enhancing takeover readiness during critical transitions. Utilizing a large-scale immersive driving simulation, the study evaluated drivers' response times and physiological measures when reacting to various alert intensities and the presence of a secondary typing task. The experiment revealed that dynamic alerts significantly improved response times and takeover performance, especially in high-distraction scenarios. Drivers responded more effectively when alerts were adjusted to their cognitive load, with strong alerts resulting in the fastest reaction times under distracted conditions. On average, dynamic alerts reduced response times by approximately 1.75 seconds compared to static alerts. Additionally, higher lateral accelerations were observed under strong alerts, indicating more decisive maneuvering. Self-rated attention-capturing scores were notably higher with dynamic alerts, particularly under strong alert conditions and in the presence of secondary tasks. The ANOVA results showed significant improvements in attention capturing and overall alert effectiveness when dynamic alerts were employed, demonstrating the robust design’s ability to capture attention and enhance driver responsiveness. The study confirmed that adaptive alert designs, which adjust based on the driver's cognitive state, can markedly enhance overall driving experience and safety. Participants reported higher levels of confidence with dynamic alerts, especially in scenarios involving secondary tasks. Despite the strong alerts, annoyance levels remained low, indicating that dynamic alerts are effective without causing undue stress. These results underscore the potential of using adaptive systems to improve safety and efficiency in automated driving, advocating for a more nuanced approach to system alerts that considers the variable cognitive states of drivers. Future research should validate these findings with on-road studies, explore a broader range of alert modalities, and refine physiological monitoring techniques to further enhance adaptive alert systems.enhuman factorscognitive ergonomicsdynamic alerthuman-machine interface (HMI)autonomous drivingdriver alertcognitive statedistractionMaster Thesis