## 6 Assessment of Integration Within Digital Twin  
### Deployment Readiness and Validation Depth  
The simulation of real-world conditions within a DT framework depends heavily on the quality of the simulated environment, which must cover various driving scenarios, including diverse lighting and weather conditions. This allows for a comprehensive evaluation of the headlamp system's behavior and performance [19; 34]. By continuously monitoring the virtual model, potential failures can be identified and addressed early, thereby avoiding disruptions in the physical system. This predictive capability contributes to reducing both downtime and maintenance costs [35; 36].  
Digital twins also play a key role in developing and validating advanced safety functionalities such as Adaptive Front-lighting Systems (AFSs), which dynamically adjust the headlamp direction based on steering input to enhance driver visibility on curves [37].  
An example of a real-world scenario simulation is illustrated in Figure 5, which shows the static driving simulator developed at Hochschule Karlsruhe (HKA). This system enables the immersive reproduction of complex road environments. Curves, varying illumination, and environmental stimuli can be reproduced consistently, making it a valuable platform for testing adaptive lighting systems in a repeatable manner. It is also conceivable that this setup lets test engineers include cases that are hard to encounter in test drives, like heavy fog, and vary the position of external illumination, like street lighting.  
Moreover, such environments support proactive system monitoring. Using embedded sensors, including cameras, the simulator can collect behavioral data and establish feedback loops between the physical and virtual models. This supports predictive diagnostics by identifying anomalies, misalignments, or delayed responses in the lighting system.  
Since the system is purely virtual, it also offers evaluation methods not available in physical test drives. As an example, it allows for the placement of virtual cameras freely in space, either static or relative to the movement of vehicles. This feature can be used to evaluate the effects of the lighting system on oncoming vehicles. By using virtual cameras, the test can be run for trucks, cars, and bicycles simultaneously by placing three different virtual cameras in the respective positions.  
The functional behavior of the headlamp system is modeled using Simulink, allowing for modular representation and early verification of control logic. This model is intended to represent the lighting subsystem independently of vehicle-level interactions. To simulate  
Figure 5: Driving simulator environment for digital twin-based headlamp validation. (**a**) shows a physical driving simulator setup with a car mock-up; (**b**) shows the driver’s view inside the simulator, displaying a virtual urban scene projected in front of the car for immersive testing.  
and validate the broader vehicle context, including sensor signals, traffic, and interaction timing, the environment was emulated in CANoe. Within CANoe, system logic is expressed using CAPL (Communication Access Programming Language), which enables flexible simulation of driving conditions, lighting commands, and error handling. This separation allows the headlamp model to be tested both in isolation (SiL) and as part of an integrated virtual vehicle environment (HiL or DT) while maintaining a clear boundary between subsystem development and system-level validation.