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The development of the future’s smart cities will increasingly depend on autonomous vehicles. For example, object detection, tracking, path planning, and sentiment or intent recognition, among other things, have all been the subject of several suggestions in recent years in an effort to address specific components of the working pipeline and develop a useful end-to-end system. This article presents a simple benchmark to evaluate the performance of object identification algorithms under deteriorating image quality. In this article, the input image is taken from the car object detection dataset, then, it gets preprocessed. The preprocessing involved Histogram equalization-based contrast enhancement, geometric transformations, and image augmentation. After getting preprocessed the region of interest is segmented with the help of a mask convolutional recurrent neural network (MCRNN). Then, the segmented region is classified using the long short-term memory (LSTM) with the self-improved chimp optimization algorithm. Object detection is performed through the proposed model called MCRNN-SICLSTM. The implementation is performed using MATLAB software. The performance of the proposed model is compared with the existing techniques using the performance metrics accuracy, precision, recall,  F -Measure, mean absolute error, mean square error, false positive rate, false negative rate. The accuracy achieved is 99.23%.

A Self-Adaptive Chimp-Driven Modified Deep Learning Framework for Autonomous Vehicles to Obtain Autonomous Object Classification