This research investigated visibility degradation caused by vehicle-generated water sprayon wet surfaces, using experimental tests, simulations, and data analysis to examine spraydynamics and their effects on camera and sensor performance.Dynamic tests faced challenges with automated contrast analysis due to insufficientresolution, lack of camera calibration, and poor lighting. Targets were too small inimages, and low contrast, even without spray, prevented reliable detection. Similar issuesaffected static tests, although higher light levels enabled more consistent results. Highbeamheadlights worsened contrast degradation by illuminating spray particles. Thesefindings emphasized the importance of proper calibration, resolution, and lighting foraccurate data collection.Outdoor tests on AstaZero test tracks showed that water depth and vehicle speedsignificantly influence spray and visibility. Deeper water (e.g., 9–10 mm) caused greatercontrast degradation than shallower water (e.g., 4–5 mm), while higher speeds amplifiedspray effects, particularly in shallow water. Variations in light conditions affected theresults, with clearer patterns emerging under stable lighting.Tyre rig tests provided detailed measurements of aerosol and water spray properties, suchas droplet size, density, and distribution. Smaller droplets (mode below 50 μm) formednear the tyre surface, while larger droplets developed downstream due to coalescence andaerodynamic forces. Higher tyre speeds and more water increased spray density andcontrast degradation. In deeper water, contrast degradation was more uniform, withnarrower ranges between maximum and minimum values.Simulations revealed key mechanisms of spray generation and propagation. Water filmdepths as low as 100 μm produced spray through capillary adhesion, with dropletsinteracting with vehicle components and airflow. Larger droplets returned to the groundquickly, while smaller droplets remained suspended, affecting visibility. Data collectedunder naturalistic conditions validated these findings and provided insights into realworldvisibility challenges.This research highlights the critical role of water depth, vehicle speed, and spraydynamics in visibility degradation. It underscores the need for improved measuringmethods, lighting, and testing protocols to enhance automated analysis and sensorperformance, especially for autonomous vehicle systems in adverse weather conditions.

 Automated vehicles and active safety functions based on sensor technology have been identified by the automotive industry as catalysts for improved safety, sustainability, accessibility, and efficiency. As technology advances, the applications of these systems are constantly expanding. Alongside these advancements, methods must be developed to evaluate and test AD and ADAS system performance and reliability in relevant and repeatable ways. This work outlines the main challenges in developing and evaluating a test method for generating road spray, a turbulent mix of fine water particles that reduce visibility caused by vehicles driving on wet surfaces. A hardware prototype and an appurtenant evaluation process were designed and produced to realize the test method. The evaluation process includes an automated software tool to quantify the prototype’s ability to degrade visibility and a method for automating sensor calibration for data collection at different locations and times. One of the key findings is the challenge of eliminating external disturbances in the test environment. Factors such as light and wind conditions significantly affect visibility through spray. The work concludes that controlling these factors is essential for achieving test repeatability. We successfully recreated road spray in a controlled environment, attenuating a sensor’s perceptive ability in steps of up to 80%, repeatedly within ±5-15%.