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, and network handshake latency is 469ms.
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.
We will evaluate the chunk image upload latency through real-world road test.
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.
