Energy-Efficient Post-Training of End-to-End Autonomous Driving Models for Edge AI Deployment
Keywords:
energy-efficient post-training; end-to-end autonomous driving; edge AI; sustainable machine learning; model adaptation; federated learning; robustness; fairness; automotive governance; lifecycle carbonAbstract
End-to-end autonomous driving models increasingly combine perception, prediction, planning, and control in a single learned pipeline. While this architecture reduces engineering interfaces, it also produces large multimodal networks whose adaptation after initial training can be costly. Edge deployment in vehicles adds stringent constraints: limited compute, memory, thermal headroom, energy budgets, and long-lived safety validation. This paper examines energy-efficient post-training as a socio-technical systems problem rather than a narrow optimization technique. It analyzes how fine-tuning, parameter-efficient adaptation, quantization-aware tuning, distillation, and federated post-training interact with automotive operating domains, fleet heterogeneity, regulatory oversight, and lifecycle carbon. The central argument is that energy efficiency in post-training cannot be achieved solely by compressing models; it requires coordinated choices across data curation, architecture, update frequency, over-the-air infrastructure, safety assurance, and governance. We discuss structural trade-offs among adaptation quality, robustness, fairness, and energy use, and we outline deployment pathways that treat post-training as a governed lifecycle process. Particular attention is given to edge AI constraints, carbon accounting, robust long-tail behavior, and equitable performance across regions and populations. The paper concludes with a research agenda for measurement, carbon-aware scheduling, reproducible benchmarks, and policy instruments that align efficient post-training with public safety and sustainability goals.
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