The Adaptive Traffic Signal Control (ATSC) System is an important component of smart city infrastructure that adjusts signal timing in response to dynamic demand using sensors, data analytics, and communication technologies. In the meantime, newly deployed Advanced Driver Assistance Systems (ADAS) can reduce driver workload and prevent crashes by alerting drivers or taking corrective action through key features such as adaptive cruise control, lane-keeping assist, blind-spot monitoring, and automatic emergency braking. The combined messages from both ATSC and ADAS may, however, increase drivers' workload, confuse them, and raise safety and security concerns that have not been sufficiently highlighted in research and practice. The proposed project will identify Human Safety Factors (HSF) with ADAS under ATSC systems through driving simulator tests, focusing on interactions between drivers and vehicle automation and addressing risks such as over-reliance, reduced attention, and poor situational awareness. The research will explore how drivers react when a vehicle's ADAS interacts with—or contradicts—dynamic traffic signals, and safely identify these cognitive friction points without real-world crash risks. The expected results will show that (1) the primary bottleneck in vehicle-to-infrastructure (V2I) deployment is no longer network latency but the human trust deficit, and (2) even if the adaptive traffic signal control system and the vehicle communicate perfectly within milliseconds, safety metrics will still fail if the human driver overrides the system due to confusion. The potential impact will be to solve the "dynamic unpredictability" problem, quantify human trust and "automation surprise", establish safe V2I stress-testing protocols, and shape future policy and industry standards.