End-to-End Autonomous Driving Network Guided by Human Perceptual Experience

Qingchao Cai, Yuanqi Su, Xiaoning Zhang, Hao Hu, Haoang Lu

Abstract: Human drivers excel at dynamically prioritizing safety-critical cues through rapid saccadic gaze shifts-an essential capability often lacking in autonomous systems. To address this gap, we propose a human-inspired framework that simulates saccadic gaze behavior to guide visual attention in driving scenarios. Specifically, our Recurrent Perception Network iteratively predicts human-like gaze regions via multiround training, achieving 85% coverage of annotated human gaze points on validation datasets. These gaze maps are then integrated into a novel Perception-Guided Driving Network, which employs cascaded attention refinement to amplify safetycritical features. Experimental results demonstrate substantial improvements over baseline methods, including 19% higher driving scores, 81.1% better traffic signal compliance, 83.3% fewer pedestrian collisions and 88.8% reduction in red light violations. These findings validate that incorporating human perceptual priors enables autonomous systems to adaptively focus on task-relevant regions in a biologically inspired manner. Our approach bridges machine perception with human cognitive strategies, paving the way for safer, human-aligned autonomous driving without sacrificing generalization.

External IDs:doi:10.1109/itsc60802.2025.11423464