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-testing methods for HMI (user experience trials, V&V metrics, simulation of user perception, etc.).
+====== 7.5 Verification and Validation of HMI ======
+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].
+===== Objectives of HMI Validation =====
+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 Methods =====
+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.
+===== Human-in-the-Loop Evaluation =====
+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.
+===== Simulation and Virtual Prototyping =====
+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.
+===== Metrics and Performance Indicators =====
+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.
+===== Towards Continuous Validation =====
+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.
+{{:en:safeav:hmc:hmi_validation_process.png?500| Example of iterative HMI Verification and Validation process from concept to field testing.}}
+===== Summary =====
+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).

en/safeav/hmc/vvhmi.1760986899.txt.gz · Last modified: 2025/10/20 19:01 by raivo.sell