Computer vision (CV) is the fundamental cognitive basis for autonomous vehicles (AVs)

facilitating the semantic interpretation of visual scenes for tasks such as pedestrian detection

traffic signal recognition

and spatial relationship assessment. Nonetheless

intrinsic limitations—such as environmental susceptibility (e.g.

performance decline in precipitation

fog

or diminished illumination)

susceptibility to adversarial scenarios (e.g.

deceptive stickers

uncommon objects)

and the inscrutable characteristics of deep learning models—demonstrate that vision alone is inadequate for resilient Perception. This study asserts that attaining safe

high-level autonomy requires incorporating computer vision into hybrid frameworks integrating multi-modal sensor fusion (LiDAR

radar

cameras)

explainable artificial intelligence for interpretability

physics-based validation

and vehicle-to-everything communication. These methodologies tackle significant weaknesses in occlusion management

long-tail situations

and real-time processing limitations. The future necessitates transdisciplinary innovation in cognitive architectures

neuromorphic Hardware

and formal safety certification to surpass pixel processing and attain human-like contextual resilience.