Efficient Parameter Adaptation of Large Models for Autonomous Driving Applications
Keywords:
parameter-efficient adaptation; autonomous driving; large models; system architecture; governance; robustness; fairness; sustainability; deploymentAbstract
Autonomous driving has become a focal point for large-model research because driving intelligence requires multimodal perception, prediction, planning, and interaction under severe safety and latency constraints. Although foundation models and transformer architectures offer broad representational capacity, adapting them to vehicle-specific operational domains remains computationally expensive and operationally fragile. This paper examines efficient parameter adaptation as a system-level problem rather than a narrow optimization technique. It analyzes adapter modules, low-rank updates, prompt-based conditioning, and post-training alignment in relation to memory budgets, inference latency, modular certification, fleet update governance, and long-term maintainability. The discussion emphasizes that adaptation choices influence not only accuracy but also safety cases, fairness across environments and populations, infrastructure cost, and sustainability. Real-world autonomous driving depends on data flywheels, simulation platforms, edge-cloud coordination, and disciplined release management, all of which shape whether parameter-efficient methods can be deployed responsibly. The paper also considers cross-domain lessons from natural language processing, robotics, and medical artificial intelligence, where modular adaptation and staged validation have become central. It concludes that efficient parameter adaptation should be treated as a socio-technical capability embedded in architecture, governance, and lifecycle policy. The most credible path forward combines parameter-efficient learning with rigorous evaluation, traceable updates, robust fallback mechanisms, and transparent accountability across manufacturers, regulators, and infrastructure operators.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
