Vision-Language Model Adaptation for Generalizable Autonomous Driving Systems
Keywords:
autonomous driving; vision-language models; domain adaptation; generalization; multimodal systems; safety governance; infrastructure; fairness; policyAbstract
Vision-language models have emerged as a promising foundation for autonomous driving systems because they can connect visual perception with semantic reasoning, linguistic instruction, and open-ended environmental description. However, adapting such models for real-world driving is not merely a matter of fine-tuning a large network on a multimodal corpus. It requires system-level decisions about architecture, data infrastructure, deployment constraints, safety validation, governance, and long-term sustainability. This paper examines vision-language model adaptation as a sociotechnical problem rather than a narrow machine learning task. It analyzes the trade-offs between modular and end-to-end autonomous driving stacks, the role of semantic grounding in improving generalization to rare and shifting conditions, and the computational and environmental costs of large-scale multimodal training and inference. The discussion then turns to operational sustainability, noting that continuous fleet learning, annotation pipelines, and over-the-air updates create dependencies that must be governed through robust engineering practices. The paper also considers fairness, accountability, and policy implications, especially when language-conditioned driving systems must explain decisions or comply with regulatory expectations. A central argument is that generalizable autonomous driving systems require more than stronger vision-language backbones; they require adaptive post-training, careful calibration, redundant validation, and institutional mechanisms that treat model behavior as part of public infrastructure. By integrating technical and governance perspectives, the paper outlines a research agenda for vision-language adaptation that is robust, deployable, and socially legitimate.
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