[raivo.sell]

Verification and Validation (V&V) of Human–Machine Interfaces (HMI) in autonomous vehicles ensure that communication between humans and intelligent systems is safe, intuitive, and consistent. While functional safety standards focus on the correct operation of sensors and control logic, HMI validation extends this to human comprehension, usability, and behavioral response [1–3].

The goal of HMI V&V is to confirm that:

• Users correctly interpret the information and cues provided by the vehicle.
• System feedback supports timely and safe human reactions.
• Communication remains effective under diverse environments and user conditions.

The validation process therefore combines *technical testing* with *human-centered evaluation*.

Verification addresses whether the interface behaves as intended. Typical methods include:

• Simulation-based testing – verification of visual, audio, and tactile signals within virtual driving scenarios.
• Scenario-based validation – predefined interaction cases between AVs and pedestrians or passengers tested systematically.
• Software-in-the-loop (SIL) / Hardware-in-the-loop (HIL) – to evaluate timing and synchronization of multimodal feedback.
• Failure mode testing – analysis of degraded communication (e.g., light or network failure) and fallback behavior [2].

Verification ensures consistency, latency limits, and redundancy across modalities before any user testing is performed.

Validation focuses on how people actually experience and understand the interface. This involves iterative testing with human participants in controlled and real-world environments [1–3]. Approaches include:

• Usability studies – measurement of comprehension time, task completion, and error rate.
• Eye-tracking and physiological monitoring – assessing attention and cognitive workload.
• Questionnaires and interviews – evaluating perceived safety, clarity, and trust.

Results are analyzed to refine signal patterns, color codes, and message phrasing to improve intuitiveness and reduce confusion.

High-fidelity simulation environments enable early-stage evaluation of HMI without physical prototypes. Tools integrate virtual pedestrians, lighting, and weather to test how design choices influence visibility and legibility [3]. Virtual validation supports:

• Rapid comparison of alternative communication concepts.
• Testing rare or hazardous scenarios ethically.
• Correlating behavioral metrics with simulated responses.

These techniques shorten development cycles and allow data-driven interface improvement.

To make validation reproducible, quantitative metrics are defined, such as:

• Comprehension rate (% of participants interpreting cues correctly).
• Reaction latency (time to respond to a signal).
• Confidence index (subjective trust level).
• Error frequency (number of misinterpretations per test run).

Standardized metrics enable benchmarking across projects and support regulatory assessment of AV communication readiness.

HMI validation does not end with prototype testing. Field data from pilot deployments provide valuable feedback loops for ongoing improvement [2]. By combining simulation, real-world performance, and user analytics, HMI systems evolve continuously as technology and user expectations mature.

Effective verification and validation bridge the gap between technical functionality and human understanding. By ensuring that communication is accurate, interpretable, and trusted, these processes contribute directly to the safe and responsible deployment of autonomous mobility [1–3].

References: [1] Razdan, R. et al. (2020). *Unsettled Topics Concerning Human and Autonomous Vehicle Interaction.* SAE EDGE Research Report EPR2020025.

[2] Kalda, K., Sell, R., Soe, R.-M. (2021). *Use Case of Autonomous Vehicle Shuttle and Passenger Acceptance.* Proc. Estonian Academy of Sciences, 70 (4).

[3] Kalda, K., Pizzagalli, S.-L., Soe, R.-M., Sell, R., Bellone, M. (2022). *Language of Driving for Autonomous Vehicles.* Applied Sciences, 12 (11).