Vehicle Prototype Optimization Engine for Rapid R&D
Rapid RPA’s Vehicle Prototype Testing Assistant accelerates the process of integrating insights from in-field vehicle tests into the R&D phase. By analyzing lessons learned from previous tests on similar models, the assistant reduces the time spent on physical prototypes while improving overall efficiency. With advanced AI and data integration, engineers can streamline issue detection and feedback processes during prototype development.
This assistant interprets driver feedback and sensor data to highlight performance issues during prototype testing. By bringing together product lifecycle data, parts lists, and IoT device data, it provides engineers with a comprehensive view of each test case. The assistant not only identifies problems but also ranks them by severity, allowing for faster decision-making and fewer iterations of physical prototypes.
Key Features:
Driver Feedback Parsing: Transform spoken or written feedback from test drivers into structured data, identifying issues for further investigation.
AI-Suggested Issue Detection: Automatically prioritize performance problems by sifting through unstructured feedback, highlighting the most critical issues.
Sensor Data Integration: Link real-time sensor data with test findings to provide a holistic view of the vehicle's performance, helping engineers focus on the most pressing areas.
By applying insights from prior models and real-time sensor data, our assistant enhances the R&D process, cutting down the time and resources needed for physical testing.
