Memory-Augmented Vision-Language Models for Context-Aware Autonomous Driving in Dynamic Environments

Abstract

Autonomous driving in dynamic environments remains constrained by the gap between momentary perception and durable contextual understanding. Vision-language models have expanded the capacity of vehicles to interpret scenes through natural-language concepts, yet most deployed pipelines still treat perception, prediction, and planning as loosely coupled modules operating on short temporal windows. This paper examines memory-augmented vision-language models as a system-level architecture for context-aware autonomous driving. It argues that memory is not merely an additional neural component but a socio-technical infrastructure that mediates continuity, personalization, infrastructure coordination, and regulatory accountability. The discussion develops an architectural framework in which multimodal encoders, episodic and semantic memory stores, retrieval mechanisms, world-model simulation, and safety monitors interact under latency, energy, privacy, and governance constraints. It analyzes robustness challenges in dynamic environments, including occlusion, adversarial conditions, long-tail events, and distribution shift, and explains how stale or overconfident memory can create new failure modes. The paper then considers deployment realities such as edge-cloud partitioning, fleet learning, communication dependency, and lifecycle sustainability. It further addresses fairness, interpretability, privacy, liability, and policy implications when vehicle memory becomes a durable record of places, people, and behaviors. The conclusion is that memory-augmented vision-language driving systems can improve context awareness, but only if they are designed as accountable infrastructures with bounded autonomy, transparent update practices, and explicit mechanisms for forgetting, auditing, and redress.