Automated Dynamic Optimization of Pneumatic Suspension Systems via Reinforcement Learning and Multi-Modal Sensor Fusion for Enhanced Ride Comfort and Vehicle Stability

**Abstract:** Traditional pneumatic suspension systems rely on pre-defined control strategies and limited sensor feedback, resulting in suboptimal performance across varying road conditions and passenger loads. This paper proposes a novel approach that utilizes reinforcement learning (RL) coupled with multi-modal sensor fusion to dynamically optimize pneumatic suspension parameters in real-time. Our framework leverages data from accelerometers, pressure sensors, strain gauges, and computer vision to create a comprehensive understanding of vehicle dynamics and road surface characteristics. By employing a deep Q-network (DQN) trained with a reward function emphasizing ride comfort and vehicle stability, the system autonomously adapts suspension settings, achieving significant improvements over conventional control methods. The proposed system is immediately commercializable, offering a tangible upgrade to existing vehicle safety and comfort features.