###### Abstract  
Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures. automous systems; decision support; computational intelligence; deep learning; object detection; classification +
Footnote †: journal: Applied Sciences  
environmental preservation and lower power utilization. Additionally, AV assists disabled and elderly persons by offering safe and reliable transport schemes [2]. Because of these considerable advantages, there has been growing attention to AVs in the academic and industrial sectors. However, designing reliable autonomous driving is a tedious process [3]. This is due to the fact that a driverless car is a smart agent, which needs to predict, perceive, plan, execute, and decide decisions in real-time, frequently in complex or uncontrolled surroundings, namely an urban area [4]. The major processes for autonomous driving are the quick and accurate identification of pedestrians, vehicles, traffic signs, traffic lights, and objects near the vehicle to ensure safety while driving. In general, AV employs different sensors, including radar, cameras, and lidar, for detecting objects [4]. The object-detection systems of AVs need to fulfill the subsequent conditions: firstly, a higher recognition performance of road objects is required. Next, a real-time recognition speed is significant for the sensors that are utilized in driving. Although certain developments have been achieved in current object-recognition technology used in self-driving vehicles, there is still a possible risk of collision as a motor car is enclosed by several objects during day-to-day use, involving static objects (signs and traffic lights) and uncontrollable moving objects (pedestrians and vehicles). Thus, it is essential to precisely calculate the significance of moving objects and quickly identify different static objects [5; 6]. Computation intelligence (CI) has had a dramatic impact on autonomous decision-making. It is a multidisciplinary study field which involves the coordination of different techniques, namely evolutionary algorithms, neurocomputing, fuzzy systems, machine learning, and artificial intelligence methods [7]. The synergy of this technique makes CI an effective mechanism for engineering applications. It could understand the complicated sensory information and consequently react.