Frameworks for Replicating Human Depth Perception in Autonomous Systems

Autonomous systems, such as self-driving cars and robotics, rely heavily on depth perception to navigate complex environments safely. However, current models for depth estimation often face limitations when dealing with real-world complexities like dynamic lighting, occlusions, and moving objects. While traditional machine learning and computer vision methods perform well in controlled settings, they struggle to replicate the adaptability of human depth perception, which seamlessly integrates multiple sensory cues and environmental context. This paper aims to bridge this gap by introducing frameworks that emulate human depth perception mechanisms to improve depth estimation in autonomous systems. We propose frameworks like the Human-Centered Interaction Framework (HCIF), Dynamic Environment Adaptation System (DEAS), and Temporal Scene Feature Adaptation (TSFA), which adapt to changing environments by leveraging cues such as motion parallax, stereopsis, and real-time adjustments to environmental changes. By mimicking human perceptual abilities, these models promise to enhance the robustness and accuracy of depth estimation in dynamic scenarios. This paper outlines the theoretical foundations of these frameworks, their practical applications, and the potential for advancing autonomous systems through the inclusion of human-like depth perception strategies.