# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 6 Conclusions  
CAVs are supposed to be a key aspect of future mobility, and the information passed by the CAVs is used to enhance the response, efficiency and comfort of the drivers. In this paper, the TLFC-RD method and the multi-classifier feature fusion-based classification of a road into drivable and non-drivable areas is achieved to determine the information about the road environment. The cross fold process at the input and the process of alterations from pixels to superpixels using pre-processing are used to minimize the difficulty while classifying the roads. Here, the optimal features from the images are obtained by using different feature extraction methods such as the spatial values of pixels, the RGB value of pixels, entropy, HSV color space, texton features, local distance distribution and LBP. Subsequently, the LeNet-5, LSTM and ResNet used in the TLFC are used to extract the feature maps, and SVM is used to classify the road and background in extreme sunlight and shadow image regions. Accordingly, the information about the environment is detected and is shared between the CAVs. From the performance analysis, we can conclude that the performance of the TLFC-RD method provides better performance than the ANN, SVM, RFC, U-Net-DAM, TFCN, TAAUWN-MV and TAAUWN-MVP. The accuracy of the TLFC-RD method for the KITTI dataset is 99.12%, which is high when compared to the TFCN and TAAUWN methods. In the future, the objects that exist in the drivable area can be identified by using deep learning classifiers. The paper investigation, resources, data curation, writing--original draft preparation, writing--review and editing and visualization were performed by P.S. and K.N.A.S. The paper conceptualization, software, validation and formal analysis were performed by B.G. Methodology, supervision, project administration and final approval of the version to be published were conducted by R.P.d.P. and M.W. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performances** & **TFCN [18]** & **TLFC-RD (K-Fold)** & **TLFC-RD (with Augmentation)** \\ \hline PRE (\%) & 98.21 & 98.93 & 99.25 \\ Sensitivity/Recall (\%) & 97.03 & 98.72 & 99.04 \\ Specificity (\%) & 96.49 & 97.01 & 98.23 \\ Max F (\%) & 96.87 & 96.31 & 99.01 \\ Accuracy (\%) & 98.27 & 99.24 & 99.71 \\ F1 (\%) & 97.92 & 98.75 & 98.93 \\ Run time (s) & 0.15 & 0.10 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of TLFC-RD method for CamVid dataset with same activation function. Figure 7: Accuracy comparison for TLFC-RD with U-Net-DAM [17], TFCN [18], TAAUWN-MV [20] and TAAUWN-MVP [20]. Funding: The authors acknowledge contribution to this project from the Rector of the Silesian University of Technology under a proquality grant no. 09/020/RGJ21/0007. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are openly available in KITTI dataset at doi:10.1177/0278364913491297, reference number [23] and CamVid dataset at doi:10.1016/j.patrec.2008.04.005, reference number [24]. Conflicts of Interest: The authors declare no conflict of interest. References
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Perception, Planning, Control, and Coordination for Autonomous Vehicles. _Machines_**2017**, \(5\), 6. [CrossRef]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON] Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles. _IEEE Trans. Veh. Technol._**2018**, _67_, 6814-6823. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles. _Sensors_**2019**, _19_, 4357. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] A real-time road detection method based on reorganized lidar data. _PLoS ONE_**2019**, _14_, e0215159. [CrossRef]
* _[PERSON] et al.