An Optimal Feature Selection and Mrblstm-based Autonomous Vehicle Detection System in Challenging Weather Conditions

Recently, self-driving cars with automation procedures such as queuing aid in traffic jams, parking assistance, lane-keep guidance, and crash evasion have been initiated. These automated driving systems and sophisticated video traffic surveillance methods laboriously rely on cameras and sensor fusion technologies. The poor performance of AVs and driver assistance systems in adverse weather conditions (ADWC), which might include snow, fog, rain, and hail, is one of the most serious problems in the development of these technologies. In order to detect items like vehicles and pedestrians in difficult situations like bad weather, it is vital to design the methods to do so. This paper proposes a deep learning (DL) model, namely a Modified ReLU-based Bidirectional Long Short Term Memory (MRBLSTM) for autonomous vehicle detection (VD) in ADWC. It is mainly composed of six stages. Initially, the proposed system collects the data from the publicly available dataset for VD. After that, image restoration is done as a preprocessing step on the collected data using the Retinex algorithm. Next, the Modified Canny Edge Detection Operator (MCO) algorithm preserves the edges from the restored images with the help of a bilateral filter and Otsu’s approach. Then, using feature extraction models for HoG, HaaR-like features, LBP, and SIFT, the features are retrieved from the edge-detected images. The optimal features are selected from the extracted features to lower the dimensions of the features using the Cauchy mutation-included Coyotes Optimization Algorithm (CMCOA). Finally, the vehicles are detected using the MRBLSTM. The proposed strategy has experimented on the DAWN dataset, and testing outcomes have demonstrated the proposed technique's efficacy, which surpasses state-of-the-art VD systems under ADWC.