Vehicle HMI Testing Using Sensor Metrics and Simulated Driving
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Solution Overview
Problem
Current human machine interface testing systems are complex to install, pose security concerns, are expensive, not mobile, and poorly predict real-world user interactions, with data from these systems being difficult to annotate and analyze collaboratively.
Innovation Solution
A computer-implemented method and system for testing and evaluating human machine interfaces by collecting user interaction data through sensors in both actual and simulated environments, analyzing this data to calculate performance metrics, and using machine learning to predict user interactions and design improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If commercially available testing systems are used, then testing capability is provided, but installation complexity increases and expert installation is required
Solution Approach 1:
The patent uses simulated environments that replicate real-world driving scenarios without requiring complex physical test setups. Instead of using expensive full-scale simulators, the system creates virtual copies of driving conditions through software-based simulations that can be deployed on standard computing hardware, thereby maintaining testing capability while reducing installation complexity.
Solution Approach 2:
The patent replaces complex mechanical testing systems with software-based solutions. By using computer vision algorithms, sensor data processing, and virtual reality environments, the system substitutes physical testing infrastructure with digital counterparts, eliminating the need for expert installation while preserving testing reliability.
2Reliability
If data is stored in individual files for security, then data security is maintained, but collaborative analysis becomes difficult
Solution Approach 1:
The patent segments data into different processing stages: raw sensor data is collected individually for security, then processed into standardized formats that can be shared collaboratively. The system divides the data pipeline into secure collection phases and collaborative analysis phases, allowing both data security and ease of collaborative operation to coexist through structured data segmentation.
3Measurement precision
If real-world testing scenarios are implemented, then predictive accuracy improves, but testing complexity and cost increase
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios through simulated environments. Instead of physically recreating every testing scenario, the system uses software-based simulations that replicate diverse driving conditions, maintaining predictive accuracy while avoiding the complexity and cost of physical test setups.
Solution Approach 2:
The patent develops a universal testing platform that can simulate multiple different driving scenarios and environmental conditions through software configuration rather than physical reconfiguration. This multi-functional system can adapt to various testing requirements without increasing hardware complexity, providing both predictive accuracy and ease of operation.
Data Source
AI summary
Described herein are computer-implemented systems and methods of evaluating control interfaces based on user interaction. The systems and methods may include: displaying, on a display, an actual environment user interface (UI) to a user; receiving, from one or more sensors, signals indicative of a first user interaction with the displayed actual environment UI; calculating a plurality of parameters of the first user interaction with the actual environment UI based on the received signals indicative of the first user interaction; and outputting an indication for the actual user interface. In some embodiments, the plurality of parameters includes one or more of: a total eyes off road time metric, a task completion time, a subtask completion time, or a performance score. In some embodiments, the actual environment UI is a test UI of a vehicle.