This week, the team made strong progress across hardware integration, software development, and system testing as we move into the final phase of the project. Overall, we remain on schedule and are preparing to complete final testing and deliver our demo next week.
On the software side, the computer vision model was fine-tuned using real pothole data, improving detection accuracy. A live dashcam preview feature was also implemented to support real-time debugging and validation of system outputs. In parallel, backend and cloud infrastructure work continued, including development and debugging of cloud functions and database integration. Field tests were conducted to verify that data from the dashcam pipeline is successfully transmitted, stored, and displayed in the web application.
On the hardware side, significant effort was devoted to integrating and debugging the GPS transponder with the Raspberry Pi and Notecard/Notehub system. While initial connectivity worked directly with the Raspberry Pi, additional challenges arose when integrating with the full hardware stack, which were actively debugged and resolved. The vehicle charging component was also completed, ensuring reliable power delivery within the car environment.
The team also collaborated on final presentation materials and conducted user testing to validate system functionality and usability. These efforts provided valuable feedback as we refine the system for the final demo. Looking ahead, the team plans to finalize hardware packaging (including the dashcam enclosure), complete remaining testing, and prepare polished demo materials.
Throughout the week, team members gained experience with tools such as Raspberry Pi, GPS transponders, Notecard/Notehub, Google Firebase, and Google Cloud Run Functions. Key learning outcomes included deeper understanding of embedded and distributed systems, cloud backend development, and the importance of consulting official documentation when working with hardware. The team also emphasized hands-on experimentation and iterative learning, enabling rapid progress while maintaining system cohesion.
Overall, the project is progressing well, with all major components coming together in preparation for final delivery.
New Tools, Knowledge, and Learning Strategies
To accomplish these tasks, the team had to learn several new tools and concepts across both hardware and software domains. These included working with Raspberry Pi systems, GPS transponders, and Notecard/Notehub hardware, as well as cloud technologies such as Google Firebase and Google Cloud Run Functions. On the software side, the team gained experience in deploying and debugging cloud-based backends, handling real-time data pipelines, and improving machine learning model performance. On the hardware side, the team developed a deeper understanding of embedded and distributed systems, particularly how multiple components communicate and operate together in real time.
To acquire this knowledge, the team relied heavily on hands-on, iterative learning. Much of the learning occurred through direct experimentation, building, testing, encountering issues, and refining solutions. Team members frequently consulted official documentation for hardware and cloud services, especially when troubleshooting integration issues. Informal learning strategies also played a key role, including reading forum posts, searching for solutions online, and watching tutorial videos to quickly understand unfamiliar tools and concepts. Additionally, the team adopted a “learn while building” approach, allowing members to make progress on the project while simultaneously developing the necessary technical skills.
Overall, these learning strategies enabled the team to quickly adapt to new technologies, overcome integration challenges, and successfully bring together all components of the system as the project nears completion.