AI-Driven Perception Pipelines for Autonomous Vehicle Navigation in Complex Environments

Abstract

Autonomous vehicle navigation in complex, dynamic environments requires accurate, robust, and realtime
perception pipelines that can sense, interpret, and predict the surrounding world. Recent advances in artificial
intelligence (AI), particularly deep learning, have revolutionized perception by enabling vehicles to integrate multimodal
sensor data such as LiDAR, radar, and camera streams into coherent scene representations. This paper reviews AI-driven
perception pipelines tailored to complex urban settings, where challenges include dense traffic, unpredictable pedestrian
movement, adverse weather, and occlusions. We classify AI-based approaches into three main categories: (i) object-level
perception using detection and tracking networks, (ii) scene-level understanding through semantic segmentation and
bird’s-eye-view (BEV) representations, and (iii) prediction and intent modeling powered by spatiotemporal learning. The
review highlights the role of advanced architectures such as transformers, graph neural networks, and multi-task learning
in improving perception fidelity. We also examine research methodologies emphasizing large-scale datasets (e.g., KITTI,
nuScenes, Waymo Open) and simulation-based pipelines for rare event testing. Key findings reveal that AI-driven perception
significantly enhances environmental awareness, but challenges remain in generalization across domains, computational
efficiency, and uncertainty quantification. We discuss both advantages—improved accuracy, adaptability, and real-time
fusion—and disadvantages—data dependency, high compute cost, and safety validation difficulties. Results from stateof-
the-art benchmarks demonstrate progress toward robust navigation, yet gaps in explainability and sim-to-real transfer
persist. The paper concludes by outlining future directions, including foundation models for perception, sensor–AI co-design,
and physics-informed learning for better generalization. Ultimately, AI-driven perception pipelines are a cornerstone of
safe and reliable autonomous navigation in complex environments, though continued interdisciplinary efforts are required
to meet deployment standards.