Currently, one of our most significant risks is that the accelerometer is producing inaccurate acceleration values, and inaccurate speed values as a result. We tried a variety of calibration techniques, but the recorded speed is off by 1mph after approximately 2 minutes. We have not yet tested the accelerometer in the car because we wanted to focus on testing the rest of the system, but we will are aiming to test the accelerometer separately this upcoming week. Fortunately, this risk is less relevant for our final demo as we decided to simulate accelerometer data that the user can control. We chose this method because it is not feasible to move the device at 5 mph in Wiegand gym while all of the other groups are testing around us. Instead, audio feedback will be determined by the user-controlled simulated accelerometer data.
Another risk that we encountered was that after integrating the different features of the CV code together, the frame rate was ~3fps, significantly lower than our goal of 5fps. This lower fps greatly reduced the accuracy of our blink detection algorithm. To reduce the fps, we would need to implement gpu parallelization or switch the other facial detection algorithms over to dlib (yawn detection and eye-direction), which would be time consuming and less accurate. Instead, we decided to remove blink-detection (inaccurate with the current low fps) as we are still able to detect when eyes are fully closed and yawning to identify drowsiness.
Since our last status report, our design changes were: removing blink-detection from our device (as mentioned above) and lowering our fps goal to 3fps based on the removal of blink detection.