Distance-based confidence generation and aggregation of classifier for unstructured road detection

Road region and non-road region separation in the unstructured road intends to be an important task for safe navigation and collision avoidance for autonomous driving vehicles. The road that connects rural areas and cities to the national highways are considered as unstructured roads. Absence of clear lane marking on these unstructured roads makes them more prone to accidents in comparison to highways, which have clear lane marking for indication of road and non-road regions. However, the unstructured roads have different color information from its background that paves an easy way for design and development of an efficient road detection system for recognition and classification of road and non-road regions. Hence, in this paper, we propose an efficient road detection system for the classification of unstructured roads into road and non-road regions using multiple nearest neighbors (NN) classifier and soft voting aggregation approach. The proposed system utilized the chromatic information (i.e. *a,*b, and Hue) to train the NN classifiers and aggregated their output using soft voting (SV) approach for final output response. The output results of multiple classifiers were aggregated using SV approach based on the confidence score obtained by each individual classifier. The performance of the proposed system is evaluated in terms of precision, recall, accuracy, intersection over union (IOU), true positive rate (TPR), and processing time and compared with current state of art methods reported in the literature. The proposed system achieved precision, recall, accuracy, IOU, and TPR of 96.79%, 96.92%, 97.8%, 96.08% and 96%, respectively with the processing time three times smaller than those of the existing state of art methods. The experimental results demonstrate that the proposed system can provide an effective guidance to the autonomous vehicles through recognition and classification of road and non-road regions in rural, urban, and city areas, wherein single unstructured roads connect the national highways.