Continual Learning Strategies for Long-Term Autonomous Driving Model Evolution

Authors

  • Primitivo Esteban Department of Computer Science, Binghamton University, Binghamton, NY, USA.
  • Aleks Kolonko Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Joacim Axelsson School of Information Technology, University of Cincinnati, Cincinnati, OH, USA.

Keywords:

continual learning, autonomous driving, model evolution, catastrophic forgetting, distribution shift, system architecture, safety governance, edge-cloud infrastructure, fairness, sustainability

Abstract

Long-term autonomous driving depends not only on the initial performance of perception, prediction, planning, and control models but also on their capacity to evolve safely after deployment. Continual learning offers a conceptual and infrastructural foundation for such evolution, allowing fleets to absorb new sensor characteristics, road geometries, traffic norms, weather patterns, and regulatory constraints without discarding prior competence. This paper examines continual learning strategies for long-term autonomous driving model evolution from a system-level perspective. It analyzes the structural trade-offs among stability, plasticity, latency, memory, compute, and safety assurance. It also considers the data and operational feedback loops that make continual adaptation possible, including fleet-scale collection, edge filtering, cloud training, validation, and staged rollout. Particular attention is given to catastrophic forgetting, distribution shift, robustness, fairness, governance, sustainability, and policy implications. The paper argues that continual learning in autonomous driving is not merely an algorithmic problem but a socio-technical infrastructure problem requiring coordinated choices in architecture, data governance, verification, and deployment. Successful long-term evolution will depend on hybrid modular designs, bounded end-to-end adaptation, auditable model lineage, human oversight, and regulatory frameworks that can accommodate incremental change without eroding public trust. The conclusion outlines a forward-looking agenda for resilient, equitable, and sustainable autonomous driving model evolution.

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Published

2026-07-26

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Vol. 1 No. 2 (2026): Journal of Engineering Systems and Digital Innovation

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