Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems

Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, driving, logistics, etc. It is highly needed to ensure the safe operations of system before launching it general public. Since design a completely challenging process, perception and decision-making act vital parts. The effective detection objects on road under varying scenarios can considerably enhance safety driving. recently developed computational intelligence (CI) deep learning models help effectively object algorithms for environment depending upon camera that exists driving systems. With this motivation, study designed novel with wild horse optimization-based recognition classification (CIWHO-ORC) model proposed CIWHO-ORC technique intends identify presence multiple static dynamic vehicles, pedestrians, signboards, Additionally, involves krill herd (KH) algorithm multi-scale Faster RCNN objects. In addition, optimizer (WHO) an online sequential ridge regression (OSRR) was applied recognized experimental analysis validated using benchmark datasets, obtained results demonstrate promising outcome terms measures.