Continual Learning and Test-Time Adaptation for Autonomous Driving Models Under Distribution Shifts
Keywords:
Continual learning; test-time adaptation; autonomous driving; distribution shift; robustness; socio-technical systems; governance; fairness; edge computing; safety assuranceAbstract
Autonomous driving models are trained under assumptions of environmental, behavioral, and infrastructural regularity that rarely hold in open-world deployment. Distribution shifts arise from weather, illumination, sensor degradation, map obsolescence, regional driving norms, construction patterns, and evolving pedestrian behavior, producing failures that are difficult to predict from static validation. This paper examines continual learning and test-time adaptation as complementary system-level strategies for maintaining autonomous driving performance under such shifts. Rather than treating adaptation as a model-centric optimization problem, the analysis emphasizes architectural trade-offs, operational governance, infrastructure requirements, sustainability, fairness, and policy implications. Continual learning offers mechanisms for retaining and updating knowledge across fleet-scale data streams, but introduces risks of catastrophic forgetting, feedback loops, memory inflation, and uneven representation across regions. Test-time adaptation enables rapid adjustment during deployment, yet can amplify errors, destabilize safety envelopes, and complicate certification when model behavior changes after release. The paper argues that robust autonomous driving requires a layered adaptation architecture that separates safety-critical invariants from tunable representations, combines cloud-based continual learning with constrained edge adaptation, and embeds auditing, rollback, uncertainty monitoring, and fairness evaluation into the update pipeline. Through cross-domain comparisons with medical imaging, industrial control, and robotics, the discussion identifies governance models and evaluation paradigms needed to make adaptive driving systems trustworthy, sustainable, and equitable across diverse mobility environments.
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