Autonomous Vehicle Perception: Deep Learning Challenges and Solutions

Abstract

Autonomous vehicles (AVs) depend on robust perception systems to interpret complex driving environments in real time. Deep learning has emerged as the dominant paradigm for AV perception, enabling breakthroughs in object detection, semantic segmentation, and sensor fusion. However, significant challenges persist, including adverse weather degradation, domain shift across geographic regions, computational constraints for real-time inference, and the long-tail distribution of rare but safety-critical scenarios. This article presents a comprehensive survey of deep learning approaches for autonomous vehicle perception, examining architectures for camera-based, LiDAR-based, and multi-modal fusion systems. We systematically analyze benchmark datasets including KITTI, nuScenes, and Waymo Open, evaluate state-of-the-art models across detection and segmentation tasks, and identify persistent gaps between research performance and deployment requirements. Our analysis reveals that while transformer-based architectures and self-supervised pre-training have substantially improved perception accuracy, challenges in real-time multi-sensor fusion, corner-case handling, and certification for safety-critical deployment remain open research problems requiring interdisciplinary solutions.