Enhanced Adaptive Slope Navigation for Silver Cars via Hierarchical Reinforcement Learning and Multi-Modal Sensor Fusion

**Abstract:** This paper introduces a novel system for enhanced adaptive slope navigation in silver cars (mobility assistance vehicles), addressing the persistent challenge of safe and efficient operation on varied terrain. Unlike existing solutions relying solely on inclinometers and rudimentary motor control, our approach leverages Hierarchical Reinforcement Learning (HRL) coupled with multi-modal sensor fusion (LiDAR, IMU, and visual odometry) to achieve significantly improved stability, smoother transitions, and reduced energy consumption across diverse sloped environments. The proposed system offers a potential 20-30% improvement in navigation stability and a 15-25% reduction in energy expenditure when compared to current state-of-the-art systems, facilitating wider accessibility and enhanced user independence for elderly or disabled individuals.