# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 6 Experimental Results and Discussion  
The results of the hybrid sensor fusion framework are presented in this section. We measure the performance using an evaluation metric appropriate for the fusion method; for our FCNx road segmentation we use the Cross-Entropy (CE) loss metric. We also show the results of implementing single object tracking using a traditional EKF fusion on our embedded edge computer. For tracking evaluation we use the root mean square error (RMSE) metric. We compare our proposed network architecture with two benchmarks FCN8 [23] and U-Net [43] networks architectures in Table 1.  
Analytically, we compare our proposed network performance with two benchmarks FCN8 [23] and U-Net [43] networks performance by measuring the Cross-Entropy loss and inference time metrics shown in Table 2. Under the same conditions, i.e., the same training input data, hyperparameter choices and computing power; our model FCNx reduces the CE loss by 3.2% compared to FCN8 model while maintaining similar inference time. We also see an improvement of 3.4% over U-net model; although the U-net network showed faster inference time but the segmentation accuracy improvement of FCNx offers a better trade off than the inference time. Table 2 results show that the FCNx achieves better free navigation space segmentation accuracy than the baseline benchmarks networks with computational complexity of 185 ms. We also compare our proposed network with two baseline benchmarks and show their detection accuracy/cross entropy loss over ten training cycles as shown in Figure 13. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Architecture** & **Skip Connection Operation** & **Residual Connections** \\ \hline FCN8 [23] & Asymmetrical encoder-decoder base & Skip connections between the encoder and the decoder using element-wise sum operator & Three convolution groups for three downsampling layers before the upsampling path \\ \multirow{4}{*}{U-Net [43]} & Symmetrical encoder-decoder base & Skip connections between the encoder and the decoder using concatenation operator & All downsampling layers information is passed to the upsampling path \\ \cline{1-1}  & Asymmetrical encoder-decoder base & Skip connections between the encoder and the decoder using element-wise sum operator & Four convolution groups for three downsampling layers before the upsampling path before the upsampling path \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of our FCNx architecture with FCN8 and U-Net architectures. Next, we show the results of our FCNx network. We tested our FCNx network on approximately 3000 road scenes and show eight of the more challenging scenes along with the ground truth and the results in Figures 14 and 15. In all scenes shown, by visual examination we can see that our model (row three) performs the road segmentation better and closer to the ground truth (row two) than the benchmark network FCN8 (row four). In Figure 14, we show four scenes from left to right: highway clear weather with no traffic, highway shadow mix with traffic, urban dim with cars parked, and urban shadow mix with cars parked. Our FCNx segmentation was more smooth at segmenting road border lines. For example, in Figure 14, it classified parts of the right-side parking area and right-side sidewalk as the road in the row four columns three and four images, respectively. It also showed some difficulty with False Positives (FP). For example, it falsely classified parts of the left-side grass area and parts of the bicycle path on the right side as the road in the row four column one and two images, respectively. Also, in Figure 15, we show four scenes from left to right: city shadow mix scene with moving bicyclist and vehicle, back-road shadow mix with no traffic, urban city road clear sky with cars parked, tunnel dim with no traffic. We again see that our network works better at the borders of the road and distinguishing between side walk and road at both near field road and the far field.