## 5 Experiments on a Real Road  
The first job to test the system on a road is code deployment, and an off-the-shelf embedded board was adopted for this. Detailed hardware specifications and system architecture of this embedded board are depicted in Figure 11.  
A USB 2.0, 720 px High Definition resolution, and 30 Frames per Second (FPS) supportive camera was connected to the board, and this camera was mounted on the passenger side mirror. Note that when we tested the mounted camera before actual testing on a road, there was no serious latency. In addition, the 7\" LCD display was connected to the board, and this screen was mounted on the  
Figure 10: Experimental results of both scenarios.  
dash board. Testing was conducted on a cloudy day with consideration of various factors such as vehicle types, vehicle colors, camera angles, and vehicle speed. In addition, hyperparameters to maximize detection accuracy and minimize false positives for testing on the embedded board changed as follows:  
* the size of a rectangle box: 6000
* the threshold value: 2
* the size of detection memory: 10  
We drove approximately 40 Miles Per Hour (MPH) on a local road and highway for about 90 min to see whether the developed system could detect all types of vehicles, such as a passenger sedan, Sports Utility Vehicle (SUV), pick up truck, van, and semi-trailer, well based on previously defined scenarios. Experimental results are quite interesting, and no system failure was found during testing. To test the first scenario, in which the system-equipped vehicle stopped or moved more slowly than target vehicles, we stopped on a local road to wait for traffic signal. Eight target vehicles passed the system equipped-vehicle in the next lane, and types and colors of the target vehicles were: two white SUVs, one grey sedan, one white sedan, two black pick up trucks, one red SUV, and one white van. We could not mechanically measure the speed of the passing vehicles, but our intuitive feeling was 35 MPH. The system showed 75% detection accuracy as six vehicles were correctly detected, but the system missed two vehicles. No false positives were reported in these experiments.  
For the second scenario, in which the system-equipped vehicle moved more quickly than target vehicles, we tested the system on a local road and highway. Our vehicle passed one black pick up truck on a local road at a speed of 35 MPH and one red, blue, and white semi-trailer on a highway at a speed of 75 MPH. We passed one grey hatchback on a highway at a speed of 75 MPH as well but, after passing this vehicle, we kept a distance between the system-equipped vehicle and grey hatchback. Note that both vehicles were driving at a speed of 75 MPH. In the experiments with the black pick up truck on a local road, the system correctly detected the truck without any false positives, thus that detection accuracy was 100%. The system showed 100% detection accuracy for all three semi-trailers,  
Figure 11: System architecture of the embedded board: The embedded board comes with 64-bit Quad-Core and Dual-Core CPU clusters, a GPU with 256 Compute Unified Device Architecture (CUDA) cores, and 8 GB 128-bit LPDDR4 RAM. The main purpose and advantage of this embedded board is used for embedded AI platform with speed and power efficiency [43], but to use a variety of features offered by the embedded board, a lot of software packages should be installed.