DeepNaviSense: Hierarchical Multi-Sensory Transformer Networks with Compositional Uncertainty Quantification for Ultra-Efficient Autonomous Ground Vehicle Navigation in Complex Dynamic Environments

Autonomous Ground Vehicles (AGVs) deployed in real-world industrial and urban environments face unprecedented challenges: heterogeneous multi-sensory data fusion, dynamic obstacle prediction, severe partial observability, sample-inefficient learning paradigms, and computational constraints on embedded systems. This paper introduces DeepNaviSense, a groundbreaking hierarchical multi-sensory transformer network architecture that integrates learnable cross-modal attention mechanisms, compositional uncertainty quantification through distributional ensemble modeling, and adaptive hierarchical policy decomposition for ultra-efficient AGV navigation. Our comprehensive framework contributes four fundamental innovations: A Multi-Scale Sensory Fusion Transformer (MSFT) that learns hierarchical spatio-temporal representations across heterogeneous sensor modalities such as LiDAR point clouds, RGB-D stereo vision, IMU kinematics, GPS localization, and ultrasonic proximity using adaptive cross-attention and temporal convolution. A Distributional Ensemble Uncertainty Network (DEUN) that separates total predictive uncertainty into epistemic (model-based), aleatoric (environment-driven), and heteroscedastic (state-dependent) components using variational inference and moment matching. A Hierarchical Option-Critic Policy Network (HOCPN) that breaks down complex navigation behaviors into interpretable sub-policies using intrinsic motivation and information-theoretic regularization. A Meta-Learned Adaptive Exploration Strategy (MAES) that dynamically balances exploration and exploitation based on multi-source uncertainty estimates. We formulate the navigation problem as a Continuous Partially Observable Semi-Markov Decision Process (C-POSMDP) and introduce new PAC-Bayesian sample complexity bounds that significantly outperform model-free approaches. Extensive experiments across six benchmark datasets—CARLA Urban-Dynamic, Warehouse-MultiAgent-2K, ROS-Gazebo-Cluttered, Factory-Floor-Real, Forest-Terrain-Outdoor, and Underground-Mine-Navigation—covering more than 5.2 million environmental interactions show that DeepNaviSense improves sample efficiency by 52.7 percent, reduces collision rates by 43.8 percent, achieves 58.3 percent better zero-shot generalization to unseen environments, and delivers robust simulation-to-real transfer with minimal domain adaptation, all while maintaining real-time performance at 45 Hertz on embedded AGV hardware. Ablation studies, theoretical analysis, and uncertainty calibration experiments confirm the effectiveness of every component. DeepNaviSense marks a major step toward safe, efficient, and interpretable autonomous navigation in safety-critical environments.