Sensor Systems and Virtual Modeling for the Development of DCAS-Equipped Vehicles

The development of vehicles equipped with Driver Control Assistance Systems (DCAS) requires the close integration of sensor technologies, vehicle dynamics, control algorithms, and virtual testing methods. This paper presents a systematic approach combining real sensor systems with virtual vehicle and environment models to support the development, verification, and validation of DCAS functions. Particular attention is paid to cameras, radar, lidar, inertial sensors, vehicle-state estimation, and driver-monitoring systems, as well as to the fusion of heterogeneous data sources.

Virtual modeling is used to reproduce the vehicle, its sensors, the driver, and the surrounding traffic environment within a unified simulation framework. Digital twins, scenario-based testing, and Software-, Driver-, Hardware-, and Vehicle-in-the-Loop methods enable repeatable assessment of assistance functions under normal, critical, and edge-case operating conditions. The proposed approach supports the evaluation of perception performance, decision-making algorithms, control interventions, and human–machine interaction before extensive physical testing is conducted.

The integration of measured data and virtual models improves development efficiency, reduces testing costs, and increases the traceability and reproducibility of validation procedures. The methodology provides a foundation for the safe and systematic development of advanced DCAS functions and their future integration into increasingly automated and software-defined vehicles.