The EVIDENT project aims to address challenges in the automotive industry's validation and verification (V&V) processes for advanced driver assistance systems (ADAS) and autonomous driving (AD) features. Traditional V&V methods struggle to keep up with the increasing frequency of software updates. The project explores virtual validation strategies to complement or replace physical testing, thereby enhancing efficiency and safety assurance.

Automotive innovations are increasingly software-driven, necessitating frequent updates. Current validation processes heavily rely on physical testing, which is time-consuming and costly. The project focuses on how vehicle functionalities could be tested and validated in simulation models and what fidelity level that could be reached. By utilizing virtual environments, the project aims to proactively test software functions before deployment, ensuring accurate assessments of system performance in diverse scenarios.

The primary goal is to develop strategies that balance the realism of virtual test environments with practical implementation. Key research questions include:

• What level of realism is required for simulations to be credible for testing edge cases?
• How can virtual testing be integrated with real-world data to discover new edge cases?
• How can virtual testing ensure functional safety to satisfy regulatory bodies?

The project also seeks to establish metrics for comparing physical and virtual test results and to utilize open-source tools for broader industry use.

The project follows a structured approach:

1. Gap Analysis: Semi-structured interviews with industry experts were conducted to identify current best practices and challenges.
2. Simulation Toolchain Assessment: Each partner's simulation tools, and maturity levels were evaluated.
3. Scenario Development: Road network representations and test scenarios were developed using ASAM OpenDRIVE and OpenSCENARIO formats.
4. Physical Testing: Various scenarios were tested on the AstaZero proving ground using vehicles equipped with advanced sensors and emergency braking systems.
5. Simulations: Partners conducted virtual tests using the respective tool chains. The simulations aimed to replicate physical test conditions and gather comparable data.
6. Data Comparison: Physical and simulated test data were compared to evaluate fidelity levels and trustworthiness. Metrics such as time to collision (TTC), braking distances, and object detection errors were analysed.

Five key case studies were tested:

1. Automated Lane Keeping System (ALKS)
2. Car-to-Car Front Turn-Across-Path (CCFTap)
3. Car in Curve
4. S-Curve
5. Occluded Child

Each scenario focused on different aspects of vehicle dynamics, sensor performance, and emergency braking responses. For instance, the Occluded Child scenario tested automatic emergency braking when a child runs out from behind parked cars.

The project identified gaps between physical and simulated test results, such as differences in braking activations between physical test and simulation. It also highlighted the need for improving simulation tools' ability to replicate real-world vehicle behaviour accurately.

Key findings include:

• Virtual tests can be reliable but require tuning to achieve higher fidelity.
• Physical tests remain crucial for validating simulation models.
• Establishing standardized KPIs for virtual testing is essential to enhance credibility.

The project faced several challenges such as:

• Variability in sensor models across partners.
• Human factors introducing inconsistencies in physical tests.
• Limitations of existing simulation tools to accurately replicate real-world scenarios.

A comprehensive list of challenges was compiled to guide future research and development efforts.

EVIDENT successfully demonstrated the potential of virtual validation for ADAS and AD features. The project contributed to developing methodologies for comparing physical and virtual tests and provided insights into the requirements for credible virtual toolchains.

Future research is recommended to focus on refining simulation validation methods, improving data synchronization methods, and addressing identified challenges to make virtual validation a practical and reliable component of automotive software development.

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.