Safety-Constrained Real-Time Decision Making for Autonomous Vehicles via NEURAL-QWEN

This paper introduces NEURAL-QWEN (Neuro-Enhanced Unified Reasoning and Adaptive Learning for Qwen-based Driving), a framework for real-time decision-making and safety constraint enforcement in autonomous driving. The framework uses a Mixture of Experts (MoE) to cut reasoning latency and cost, Adaptive Low-Rank Decomposition (AdaLoRA) to avoid catastrophic forgetting, and a Time Fusion Transformer (TFT) for trajectory prediction. It also uses federated multi-agent coordination with differential privacy for secure knowledge sharing, and Constitutional AI safety constraints to enforce driving rules. The framework uses reinforcement learning with human feedback (RLHF) to align simulated and real-world driving, and a semantic attention mechanism (SAM) for cross-modal reasoning. NEURAL-QWEN combines fast performance with safety standards and gives a solution for safe and interpretable decision-making.