Self-driving technology is rapidly transforming the transportation industry; however, existing autonomous navigation approaches often address safety, explainability, or regulatory compliance in isolation, limiting real-world trust and deployability. This paper proposes a Responsible Artificial Intelligence (RAI) framework that unifies safety assurance, explainability, and policy compliance within a single decisionlevel architecture, rather than treating these aspects as post-hoc or loosely coupled add-ons as seen in prior work. Safety is ensured through risk-aware decision modeling and real-time uncertainty monitoring, while navigation transparency is achieved via a combination of intrinsic interpretability and posthoc explanation mechanisms. In contrast to existing Responsible AI approaches that lack operational policy enforcement, the proposed framework integrates rule-based ethical constraints and regulatory ontologies directly into trajectory planning to ensure continuous legal compliance. Experimental evaluation using simulation and benchmark driving datasets demonstrates that the proposed approach significantly improves safety awareness, decision transparency, and regulatory adherence without compromising navigation efficiency. These results position Responsible AI as a practical enabler of trustworthy, deployable autonomous navigation systems.