Safety-Constrained Policy Optimization for End-to-End Autonomous Driving Using Offline Reinforcement Learning

Abstract

End-to-end autonomous driving systems increasingly combine perception, prediction, planning, and control within learned computational pipelines, raising the possibility of more adaptive behavior but also intensifying problems of safety assurance, interpretability, and regulatory oversight. Offline reinforcement learning offers a compelling route for such systems because it allows policies to be trained from previously collected driving data without requiring unsafe online exploration. Yet offline learning alone does not resolve the deeper socio-technical challenge of constraining policy behavior under rare, ambiguous, and ethically charged conditions. This paper examines safety-constrained policy optimization as an architectural and governance strategy for end-to-end autonomous driving using offline reinforcement learning. It argues that safety constraints should not be treated as post hoc filters or narrow reward penalties but as structural commitments that shape data curation, model architecture, training objectives, evaluation protocols, deployment boundaries, and accountability mechanisms. The analysis connects technical advances in offline reinforcement learning, constrained policy optimization, end-to-end driving, simulation infrastructure, and formal safety models with concerns about robustness, fairness, sustainability, and public legitimacy. It also considers how fleet-level learning, over-the-air updates, edge computation, and regulatory auditing create new dependencies between engineering choices and institutional capacity. The paper concludes that safe end-to-end autonomy requires a layered system in which constrained offline policy optimization is embedded within transparent operational design domains, continuous validation, human oversight, and enforceable governance. Without such integration, offline reinforcement learning may improve sample efficiency while leaving unresolved the structural risks that determine whether autonomous driving can be trusted at scale.