An autonomous vehicle can complete its route while disrupting surrounding traffic. We introduce SAFE-AD, augmenting reinforcement learning (RL) observations with compact descriptors of a finite-horizon interaction-pressure field constructed from current traffic states. The complete objective supplements the environment's task reward with risk, comfort, and externality costs. Then, we formulate a context-conditioned physics-informed neural network (PINN) to approximate the numerical field. Evaluation combines held-out naturalistic recordings, HighwayEnv diagnostics, and closed-loop MetaDrive tests in various traffic scenarios, with a matched factorial experiment with continuous soft actor-critic (SAC) separates observation and objective effects. At unprotected intersections, the complete profile reduces collision rate from 0.296 to 0.220 and mean absolute jerk by 0.98 m/s³ relative to baseline SAC. Besides, the mean absolute jerk in roundabout decreases by 1.54 m/s³. The results show scenario-specific gains and sensitivity to objective calibration.  

Federated Learning (FL) is increasingly deployed in healthcare to enable collaborative intelligence while keeping sensitive data privately at local institutions. However, existing healthcare-oriented FL frameworks still suffer from several limitations: they are vulnerable to adversarial model updates, provide limited transparency and verifiable auditability, and often lack predictable performance under constrained resources. We present IIN-Health, a blockchain-enhanced intelligent fusion network tailored for dependable healthcare FL. IIN-Health adopts a dual-chain architecture with policy-aware access control and auditable provenance tracking to integrate learning, security, and governance in a unified framework. Evidence-Carrying Access Tokens (ECATs), combined with zero-knowledge proofs, are introduced to enforce patient-defined policies and validate access decisions without disclosing sensitive information. In addition, we design MedBFT-Δ, a domain-specific Byzantine fault-tolerant protocol that ensures reliable system behavior in the presence of faulty or malicious participants. We conduct several experiments to validate its feasibility and accuracy on the MNIST dataset. The results demonstrate that IIN-Health achieves smooth and stable convergence, exhibits strong resilience against poisoning attacks, and maintains graceful performance degradation under resource constraints, while preserving verifiable auditability of model updates and data flows. These observations indicate that IIN-Health can provide a practical balance among performance, security, and regulatory compliance, and thus offers a promising foundation for trustworthy and scalable FL deployments in healthcare.