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Christine’s Status Report for 4/19

Progress

  • Implemented API key for dash cam-related endpoints (PR).
  • Implemented post-assembly cleanup logic (PR).
  • Added chunk upload validations for data integrity (PR).
  • Added tests for edge cases (duplicated chunks, out-of-order uploads, invalid requests) to enhance test coverage (PR).
  • Implemented GET /plates endpoint for dash cam latency benchmark (PR).

Tools & Knowledge Learned

I had to learn AWS from scratch. I used a mix of informal learning strategies: online videos and tutorials helped me get started with AWS services. AWS CDK documentation and online code examples were extremely helpful. Additionally, GitHub Copilot helped me write tests more efficiently, and GPT was invaluable in clarifying any conceptual questions I had about AWS. These resources and tools really helped me navigate the challenges of my project and significantly boosted my understanding of cloud computing.

Status:

  • Currently on track with all planned objectives.
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Andy’s Status Report for Apr 19

Learning Reflections

As I progressed through the design, implementation, and debugging phases of our project, I encountered several hardware components that required me to acquire new technical knowledge and hands-on skills for the bring up of them. Specifically, I faced challenges in bringing up the Raspberry Pi (RPi) system, the RPi camera module, the uninterruptible power supply (UPS) module, and the Blues Notecard module.

To overcome these challenges, I adopted a multi-pronged learning strategy:

  • Official Documentation: For each hardware module, I began by reading the official documentation. This provided a solid baseline for understanding the intended setup procedure and configuration parameters.
  • Online Video Tutorials: I found YouTube tutorials especially helpful for the RPi camera and Blues Notecard bring-up process. These informal resources often included demonstrations on how to install the modules properly that helped me troubleshoot physical connections, and install proper software dependencies.
  • Community Forums and Stack Overflow: When encountering unexpected issues (such as I2C readings from the UPS module), I found useful informations on Stack Overflow from a similiar question discussion before.

Summary of Work This Week:

This week is mainly integration testing and final wrap-up and final documentation preparation. I did the power adapter system integration testing in-vehicle. I measured output voltage and current of the adapter during driving conditions to verify power stability and confirmed that the UPS output to the Raspberry Pi 5 consistently delivered the required 5V 5A, ensuring reliable operation.

At the same time, I worked together with my teammates on assembling slides and visuals for the final presentation and began preparing talking points and demo setup for the final showcase.

Progress:

We are currently on progress, we finished most system integration testing, and are ready to prepare for final demo and documentation preparation.

Next Steps and Schedule:
• Record final demo footage and supporting visuals for the video presentation.
• Deliver final presentation and participate in demo day.

We are currently on track

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Vicky’s Status Report for 4/19

Personal Accomplishments

    • Dash Cam Bringup:
      • Improved the image upload pipeline by adding auto retry on failure
      • Included API Key for GET and POST requests to add security
    • Dash Cam Verification:
      • Verified that the dash cam’s weight is 0.77lbs
      • Verified that the system’s startup script executes within 33s of a power change, recording starts within 41s of a power change, and shutdown script executes within 332ms of a power change
      • Verified that the system reliably transitions its ALPR setting (opt-in/out) within 1s (instantaneous)
      • Verified that the data at rest on the dash cam is erased upon shutdown

Progress

My progress is on schedule.

Schedule

  • Final poster, report, and video
  • Inference demo script

Tools and Knowledge Learned

The learning strategies that are most helpful to me includes: online videos for dash cam bringup, forums for specific debugging questions, and ChatGPT for conceptual questions.

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Vicky’s Status Report for 4/12

Personal Accomplishments

    • Dash Cam Bringup:
      • Added a dip switch and software script to allow users to opt in or opt out of the ALPR mode
      • Refactored GPS code to make it nonblocking when GPS signal is not detected and debugged GPS connection issues on power on
      • Implemented sync match image chunk upload and experimented with async match upload using thread
        • Both HTTP GET and HTTP POST use the same I2C address, so I have to implement a locking mechanism
        • This results in a traffic jam that we think is not worth for implementing async chunk image upload
        • So we decided to stop further inferencing when we are uploading a match image
    • Dash Cam Testing:
      • Added an LED to indicate the stage of the dash cam for easier road testing, LED is ON during setup, LED flashes slowly during recording, and LED flashes faster during inference
      • Wrote a pure recording script to record real-road footage for future demos
      • Worked with Christine and Andy to conduct a 30 minute road test under rainy setting
    • Dash Cam Verification:
      • Verified that the dash cam records continuously in 1-minute clips for 10 minutes in a stationary setup
      • Verified that the dash cam records continuous footage under five separate real-driving tests of at least five minutes each
    • ALPR Verification:
      • Benchmarked the YOLOv11n model using a curated set of 386 real-world images sourced from the Kaggle-based dataset, achieving 90.4% mAP50
      • Benchmarked the PaddleOCRv3rec component on the scraped dataset from platesmania.com, which includes 4,000 cropped synthetic U.S. license plate images and 1,000 cropped real-world images, achieving 93% accuracy
      • Benchmarked the end-to-end ALPR pipeline by passing 5,000 full-frame, real-world images from platesmania.com through the system, achieving 79% accuracy, with 17% false positive and 4% false negative

Progress

My progress is on schedule.

Schedule

  • Road tests
  • Dash cam pipeline fixes (API key integration, etc.)
  • Scripts for demo purposes
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Christine’s Status Report for 4/12

Progress

  • Implemented Chunk Upload Protocol (PR): Successfully developed and integrated the chunk upload protocol. For detailed information on the implementation and updated flow, please refer to the PR description.
  • Added Match Log (PR): Developed and added a match log feature to track and record relevant data, enhancing our ability to monitor and analyze system performance and matches.
  • Integration Test with the Team: Collaborated with the team to conduct integration testing.
  • Currently on track ✅

Verification

  • Watchlist Management: Added manual tests and Jest automated integration tests to cover both the happy path and edge cases, such as invalid input and missing API keys.
  • Watchlist Query Requests: Implemented manual tests along with Jest automated unit and integration tests to handle cases involving matched plates, unmatched plates, and invalid inputs.
  • Image Upload: Added Jest integration tests for the end-to-end image upload process to ensure comprehensive coverage and reliability.
  • To-do:
    • Chunked Image Upload: Add Tests for Edge Cases (#14):
      • Develop and execute tests for various edge cases to ensure the robustness of the chunk upload protocol, including:
        • Scenarios where chunks are missing or uploaded out of order.
        • Handling invalid requests, such as those with missing fields in the /uploads API.
        • Managing duplicated chunks to prevent errors and ensure data integrity.

All tests are integrated with our CI/CD pipeline, ensuring continuous verification and validation of the system as changes are made.

What’s Left to Do:

  • Implement API key for dash cam communications
  • Add tests for chunked image upload edge cases
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Team Status Report for 4/12

Validations Done

  • We conducted five in-vehicle tests to confirm that the car cigarette lighter reliably powers the unit for 30-minute sessions.
  • In real-world testing, we deployed the dash cam in a vehicle for a 30-minute drive, during which the driver continuously called out the license plate numbers observed. During the 30-minute session, the driver identified 25 license plates while driving. All 25 were correctly detected by the dash cam system. In addition, the dash cam detected an extra 117 license plates that the driver was unable to observe while driving.
  • To evaluate end-to-end system latency, we conducted 10 field tests and record timestamps at key stages. Average on-board detection latency is 123ms, OCR latency is 134ms, watchlist query latency is 469ms, and image upload latency is 7332ms.

Validations To Do

  • In the lab, we will verify that the unit’s weight is less than 1.5lbs using a scale.
  • We will time the initial setup process with five participants, ensuring that the average setup time is under 10 minutes.
  • We will verify that the system’s startup and shutdown scripts execute within 30s of a power change through five simulated tests and five field tests, ensuring that no file corruption occurs during the process.
  • We will test the opt-in switch functionality through 10 separate activation/deactivation cycles to confirm that the system reliably transitions its ALPR setting within 1s.
  • We will verify that data at rest on the dash cam is erased upon shutdown, that non-match data on the server is deleted immediately, and that matched data is retained for 21 days.

Risks

  • Since we are now in the testing phase, we find calculating our performance data quite challenging. It involves a lot of manual work as we need to look through footage to identify additional license plates that the dash cam caught.

Changes

  • We modified the image uploading pipeline due to a hardware limitation with I2C bandwidth. Initially, we planned for two parallel threads on the dash cam: one for uploading chunked images upon server request and another for ongoing inference and license plate number queries. However, our hardware supports only one active I2C communication at a time, preventing simultaneous uploads. To address this, we now pause inference and plate number query during image uploads, resuming them afterward. With an average upload time of 500ms, the impact on real-time detection is minimal. This approach also makes sure we prioritize plates on watchlists, as the chance of detecting another watchlist plate immediately after is very low.
  • We removed loop recording since it’s not the main focus of the project and it’s challenging for development and testing. Similarly, we decided to remove text formatting as current performance metrics looks good and it’s not the main focus of the project.
  • For security compliance, we’ve added an API key to the dash cam communication APIs. This ensures only authorized devices can access the system (send detection query / upload images). We can implement rate limiting to prevent API abuse. If necessary, we can trace back activities to specific dash cams.
  • Since we have limited bandwidth, we decided to cut the server load testing to conserve both budget and effort. This allows us to better focus on the integration and presentation.

Schedule

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Andy’s Status Report for 4/12

Verification and Validation Update:

As we transition into the verification and validation phase, we are aligning our testing approach with our core engineering design and use case requirements. I am mainly in charge of the dashcam circuit components so my individual testing are more oriented with the dash cam power supply system.

Our dashcam power system is designed to draw power from the car’s cigarette lighter, which is converted by an adaptor to 8.4V 2A for the UPS(Uninteruptable Power Supply). The UPS then regulates this input to provide a stable 5V 5A output to the Raspberry Pi, while also managing seamless switching to its internal battery when the car is turned off—ensuring a safe shutdown process for the RPi.

Tests Completed:

  • Power Regulation Test: Verified that the adaptor consistently provides stable output (8.4V ± 0.3V and 2A ± 0.2A) under both nominal and surge input conditions to the RPi UPS module.
  • UPS Power Supply Test: Verified that the UPS consistently provides stable output (5V ± 0.3V and 5A ± 0.4A) under both nominal and surge input conditions from the simulated power supply from car. Also I tested and verified that the UPS consistently provides stable output when the power supply simulated from the car is off, which means the UPS switched to its battery for backup power supply.

After verified the system works during simulation test in the lab, I tested the system on a car with actual car cigarette lighter.

  • In-Vehicle Test: Validated system stability in a real car environment with varying RPMs and load conditions. Continously measured the voltage for 5 min while another driver is driving the car under normal driving condition.

Verification Approach:

  • Instrumentation: Use multimeters, current clamps to monitor real-time voltage and current waveforms.
  • Simulation: Use voltage generator from ECE labs

Planned Tests:

  • Endurance Test: Run the complete system for an extended period to test long-term reliability, focusing on consistent voltage and system uptime.
  • Integration Test: Connect with RPi to conduct end-to-end testing for the entire system while continously measure the voltage and current to check for abnormal circuit behaviors.

Summary of Work This Week:

  • Cigarette Lighter to Adaptor Simulation Testing:
    • Used a wire-to-car-cigarette-lighter converter to simulate a car’s power source.
    • Connected our adaptor to a breadboard to measure voltage and current under standard conditions.
    • Confirmed that the adaptor correctly regulated the voltage from the car cigarette lighter.
    • Simulated transient input spikes ranging from 10V to 20V, which can occur due to vehicle alternator behavior or load switching.
    • Verified that our adaptor provided clean, regulated power even during input fluctuations.
  • Raspberry Pi UPS Power Test:
    • Used the stabilized adaptor to power a Raspberry Pi UPS HAT.
    • Disassembled the RPI and UPS HAT for isolated testing.

Next Steps and Schedule:

  • Continue full integration testing with the RPI in-vehicle.
  • Monitor long-term stability under varying driving conditions.
  • Begin logging power behavior during startup and shutdown phases.

Progress:

This week, I made progress by completing the majority of the circuit-level testing. I verified the power regulation behavior of our adaptor and UPS module, both in simulation and in a real vehicle environment. With these components confirmed to be working, we are ready to move forward with full system integration using the car cigarette lighter as the actual power source.

Additional tests, including endurance and thermal testing, are planned to further ensure reliability under extended and realistic conditions.

 

 

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Vicky’s Status Report for 3/29

Personal Accomplishments

  • CI/CD:
  • Dash Cam Bringup:
    • Integrated HTTP GET code to end to end script and got cache working to reduce the number of requests
    • Integrated Andy’s UPS auto shutdown code to end to end script
    • Integrated Sparkfun GPS-RTK module via USB serial to acquire GPS coordinates in the end to end script
      • I2C connection was unsuccessful due to address conflict with the UPS module
      • UART connection was unsuccessful for unknown reasons
    • Added logging to end to end script, with auto ALPR on when power on and board shutdown when unplugging the cable
    • Improved end to end script to only run OCR on horizontal detection boxes with >0.5 confidence and only send GET requests when recognized plate length is between 2 and 8 to reduce the number of requests
    • Assisting Blues engineer to debug the GET json error
  • Dash Cam Testing:
    • Set up the dash cam to be installable on a car with a phone holder
    • Worked with Christine and Andy to road test the dash cam in real world setting and validated ALPR and GET request capabilities

Progress

My progress is on schedule.

Schedule

  • Central server integration and testing
  • Opt-in switch module bringup and testing
  • End-to-end pipeline debugging
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Team Status Report for 3/29

Risks

  • Chunked image upload: The chunked image upload process is fairly complicated because we’re implementing it from scratch. While we considered S3 multipart upload, it wasn’t suitable for our file sizes. This approach introduces more moving parts, including chunk splitting, metadata tracking, individual uploads, and image assembly. While this complexity increases the potential for issues (e.g., missing chunks, incorrect assembly), we’ve developed a solid design for the image upload protocol this week (details in PR description) and got the basic structure in place. The focus next week will be on thoroughly testing and debugging to minimize risks and ensure reliability before integration.
  • Power supply integration: Integrating our uninterruptible power supply (UPS) with car cigarette lighter power sources presents potential challenges, as the UPS currently operates reliably only from stable stationary power outlets. We have procured an adapter and an additional cigarette lighter wiring adapter specifically for testing purposes. Before any on-road implementation, we plan extensive lab testing to verify functionality, stability, and safety under simulated automotive conditions. This cautious approach aims to mitigate risks associated with unstable voltage, unexpected power fluctuations, or hardware incompatibility when connecting to a vehicle’s power system.

Changes

  • GPS module: We changed the GPS module from Adafruit Automate GPS to Sparkfun GPS RTK according to Professor Tamal’s recommendation. 
  • Image upload protocol: We realized that Blues Notehub’s 8KB file size limit requires us to use chunked uploads for images (~45KB). Originally, we implemented S3 pre-signed URLs for full image uploads, but that approach wouldn’t work with the size limitations. As a result, we’re implementing the chunked image upload process from scratch. 
  • Partial match instead of message queue: Instead of implementing a message queue in the watchlist query server for asynchronous processing, we are prioritizing local filtering (partial matching) on the dashcam for now since this approach can more directly reduce bandwidth consumption and server load. By performing partial matching directly on the dashcam, we can simplify server-side operations, minimize data transmission, and enable faster, more efficient processing at the edge. The dashcam will locally store a synced watchlist and perform partial matches (e.g., comparing the first few characters of license plates) before sending only the relevant matches to the server. We still need to figure out the best way for the dash cam to sync with the server.

Schedule

Pics!