Uncertainty-Aware Fine-Tuning for Safe End-to-End Autonomous Navigation
Keywords:
uncertainty-aware fine-tuning; end-to-end autonomous navigation; safety assurance; runtime monitoring; socio-technical systems; AI governanceAbstract
End-to-end autonomous navigation systems increasingly rely on large neural models that map raw sensor streams to driving decisions, yet their deployment in safety-critical settings remains constrained by opaque uncertainty, distribution shift, and brittle post-training adaptation. This paper examines uncertainty-aware fine-tuning as a socio-technical capability rather than a narrow model optimization technique. It argues that safe end-to-end navigation requires uncertainty representations to be embedded across data curation, model adaptation, runtime assurance, fleet operations, governance, and sustainability planning. The analysis begins by distinguishing aleatoric and epistemic uncertainty and explaining why calibration, ensembles, and Bayesian approximations matter for vehicular perception and planning. It then considers fine-tuning architectures that balance task specialization with safety preservation, including simulation-based adaptation, conditional imitation, and post-training procedures that can erode or enhance robustness. The paper discusses infrastructure requirements for continuous validation, over-the-air updates, edge inference, and human oversight, emphasizing that uncertainty-aware fine-tuning changes the economics of testing and the distribution of liability. Cross-domain comparisons with aviation, medical artificial intelligence, and industrial robotics clarify how assurance cases, operational design domains, and fairness audits can be adapted to autonomous mobility. The conclusion proposes that uncertainty-aware fine-tuning should be governed through traceable datasets, calibrated confidence thresholds, fleet-level monitoring, and inclusive policy mechanisms. Without such system-level discipline, fine-tuned autonomy may achieve impressive average performance while remaining unsafe in rare, ambiguous, or socially uneven conditions. The paper contributes a structural agenda for researchers, engineers, regulators, and operators who must translate uncertainty estimates into deployable safety arguments.
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