Uncertainty-Aware Post-Training of Autonomous Driving Models for Safety-Critical Decision-Making

Abstract

Autonomous driving models increasingly rely on large-scale representation learning, end-to-end policy learning, and data-driven perception to operate in open-world traffic environments. Yet safety-critical decision-making cannot be reduced to average-case predictive accuracy. This paper examines uncertainty-aware post-training as a system-level strategy for improving the safety, robustness, fairness, and governability of autonomous driving models after initial pre-training and before or during deployment. The argument is that post-training should not be treated merely as a final optimization stage for task performance, but as an architectural and institutional process that shapes how models represent ambiguity, defer to fallback systems, communicate confidence, and respond to distributional shift. The paper analyzes key sources of uncertainty in driving, including perceptual ambiguity, behavioral indeterminacy, environmental novelty, and socio-legal opacity. It then discusses how uncertainty-aware post-training can support calibrated decision thresholds, selective prediction, risk-sensitive policy adjustment, and human oversight. Particular attention is given to structural trade-offs among latency, compute cost, interpretability, and safety margins, as well as to the governance implications of deploying uncertainty-aware systems in mixed human and autonomous traffic. The paper also considers infrastructure requirements, evaluation regimes, sustainability concerns, and regulatory accountability. It concludes that uncertainty-aware post-training is necessary but insufficient unless integrated into broader assurance ecosystems that combine technical calibration, operational monitoring, ethical governance, and public accountability. Such integration is essential for autonomous driving systems that must act conservatively under ambiguity while remaining viable at scale.