The perception of the vehicle’s environment is crucial for automated vehicles. Therefore, environmental sensors’ reliability and correct functioning are becoming increasingly important. Current vehicle inspections and self-diagnostics must be adapted to ensure the correct functioning of environmental sensors throughout the vehicle’s lifetime. There are several promising approaches for developing new test methods for vehicle environmental sensors, one of which has already been developed in our previous work. A method for testing vehicle front cameras was developed. In this work, the method is improved and applied again. Various test vehicles, including the Tesla Model 3, Volkswagen ID.3, and Volkswagen T-Cross, are stimulated by simulating driving scenarios. The stimulation is carried out via a tablet positioned before the camera. The high beam assist is used to evaluate the vehicle’s reaction. It was observed whether the vehicle switched from high to low beam as expected in response to the stimulation. Although no general statement can be made, the principle of stimulation works. A vehicle reaction can be successfully induced using this method. In further test series, the influence of display brightness is examined for the first time in this work. The results show that the display brightness significantly influences the test procedure. In addition, the method is validated by stimulation with colored images. It is shown that no complex traffic simulation is necessary to trigger a vehicle reaction. In the following validation approach, the CAN data of the Tesla Model 3 is analyzed during the tests. Here, too, the assumption that the vehicle reaction is based solely on the detected brightness instead of identifying road users is confirmed. The final validation approach examines the method’s applicability to other vehicles and high beam assist technologies. Although the method could not be used on the Volkswagen T-Cross due to a fault detected by the vehicle’s self-diagnosis, it worked well on the Volkswagen ID.3. This vehicle has a dynamic light assist in which individual segments of the high beam are dimmed during stimulation. Although the method developed to stimulate vehicle front cameras is promising, the specific factors that trigger the vehicle responses remain to be seen. This uncertainty suggests that further research is needed better to understand the interaction of stimulation and sensor detection.

Cooperative intelligent transportation systems continuously send self-referenced data about their current status in the Cooperative Awareness Message (CAM). Each CAM contains the current position of the vehicle based on GPS accuracy, which can have inaccuracies in the meter range. However, a high accuracy of the position data is crucial for many applications, such as electronic toll collection or the reconstruction of traffic accidents. Kalman filters are already frequently used today to increase the accuracy of position data. The problem with applying the Kalman filter to the position data within the Cooperative Awareness Message is the low temporal resolution (max. $10 \mathrm{H}\mathrm{z}$) and the non-equidistant time steps between the messages. In addition, the filter can only be applied to the data retrospectively. To solve these problems, an Extended Kalman Filter and an Unscented Kalman Filter were designed and investigated in this work. The Kalman filters were implemented with two kinematic models. Subsequently, driving tests were conducted with two V2X vehicles to investigate and compare the influence on the accuracy of the position data. To address the problem of non-equidistant time steps, an iterative adjustment of the Process Noise Covariance Matrix $\mathit{Q}$ and the introduction of additional interpolation points to equidistance the received messages were investigated. The results show that without one of these approaches, it is impossible to design a generally valid filter to improve the position accuracy of the CAM position data retrospectively. The introduction of interpolation points did not lead to a significant improvement in the results. With the $\mathit{Q}$ matrix adaptation, an Unscented Kalman Filter could be created that improves the longitudinal position accuracy of the two vehicles under investigation by up to 80% (0.54 m) and the lateral position accuracy by up to 72% (0.18 m). The work thus contributes to improving the positioning accuracy of CAM data for applications that receive only these data retrospectively.

As automated driving functions based on environmental sensors become increasingly deployed, ensuring reliable performance over the vehicle lifetime is essential. Currently, verification is carried out through internal self-diagnostics, which do not always operate correctly, and periodic technical inspection, which assesses only the test criteria installation and condition. Test criteria for function and efficiency of environmental sensors are neither standardized nor routinely assessed, creating the need for new testing approaches. Previous low-cost research approaches defined a method and conducted experiments to verify a vehicle’s front camera by displaying visual stimuli and evaluating the high beam assist response. Whereas the camera’s function could be verified through a basic qualitative check, the approach did not enable a quantitative evaluation of its performance. The aim of this work was therefore to advance this approach and investigate the added value of a Vehicle-in-the-Loop test bench for front camera verification. Three tests were conducted. A supporting method was introduced to reproducibly detect and define the position of the headlight cutoff line, enabling consistent evaluation of the vehicle’s reaction. With static camera stimuli (Test I), the function of the front camera could be verified, and the influence of the vehicle geometry on the reaction was assessed. Dynamic stimuli (Test II) additionally enabled an efficiency evaluation, allowing quantitative comparison between vehicles. However, transferring the stimuli into a reproducible virtual simulation (Test III) remained challenging, as the vehicles under test did not respond consistently. Further research is required to refine and simplify the method toward a standardized periodic technical inspection procedure.

Advanced Driver Assistance Systems (ADAS) and automated vehicles rely on environmental sensors whose correct operation is safety-critical throughout the vehicle lifecycle. However, standardized in-use verification concepts for vehicle perception and ADAS are still missing. Even within Periodic Technical Inspections (PTI), no standardized tests currently exist to verify the function, efficiency, or perception performance of environmental sensors or ADAS. This gap creates a safety risk for road traffic and Intelligent Transportation Systems (ITS), and international expert groups are working toward harmonized inspection solutions. Thiswork summarizes existing approaches for assessing the operational performance of environmental sensors and ADAS and highlights major limitations such as high investment costs, restricted data access, limited scalability, and limited evaluation depth. Building on these findings, a novel target-based method supported by Car-to-Everything communication is introduced. It enables a holistic, system-level evaluation of vehicle perception by comparing perceived target attributes with a defined ground truth generated by a guided reference target. All considered approaches—including the proposed method—are categorized, evaluated, and compared using thirteen qualitative criteria. The results indicate that the proposed method does not rely on proprietary sensor data and can assess both function and efficiency within the PTI framework. Furthermore, the method is cost-effective and highly scalable. Further research is required, particularly regarding practical implementation and validation.