This review article explores the progress and challenges in AI-driven object detection and wayfinding for remote-controlled and driverless cars. Incorporating artificial intelligence is becoming increasingly important as the need for safer and more efficient transportation systems grows. We explore state-of-the-art approaches with an emphasis on their use in real-time dynamic pathfinding and object recognition, including deep learning models such as convolutional neural networks (CNNs) and novel transformer-based architectures. We examine current capabilities, limitations, and the influence of environmental conditions on navigation reliability and detection accuracy. We further highlight research gaps and provide a dynamic framework for enhanced safety and responsiveness in autonomous navigation that integrates reinforcement learning and multimodal data sources. This study aims to contribute to the current discussion on improving the efficiency and security of self-governing systems in various metropolitan environments. This method of object detection is used to recognize different categories from the previous dataset. Humans can analyze the rules to follow on roadways, but animals analyze the rules so they come in front of automobiles and our ideas to prevent accidents.