Vision-Language Model Enhanced Trajectory Generation for Autonomous Vehicles
Keywords:
vision-language models; trajectory generation; autonomous driving; multimodal learning; socio-technical systems; robustness; governanceAbstract
Trajectory generation for autonomous vehicles has traditionally been framed as a geometric and probabilistic inference problem, yet the growing complexity of urban environments demands systems that can reason about intent, social convention, and context in ways that purely numerical representations struggle to support. This paper examines the emerging integration of vision-language models into trajectory generation pipelines for autonomous driving, treating the resulting systems as socio-technical infrastructures rather than isolated perception modules. We analyze how large-scale vision-language pretraining contributes semantic grounding that improves behavior prediction in ambiguous, high-density, and interaction-rich scenarios, and we consider the architectural trade-offs that arise when language-conditioned representations are coupled with real-time motion planners. The discussion extends to data infrastructure, scenario curation, and the long-tail problem, arguing that dataset composition and annotation governance are as consequential as model design. We further examine deployment constraints, including latency budgets, onboard energy consumption, sensor degradation, and distributional shift across geographies, and we connect these engineering concerns to questions of fairness, accountability, sustainability, and regulatory oversight. Drawing on literature spanning transformer architectures, multimodal pretraining, motion forecasting benchmarks, and AI ethics governance, the paper proposes that the central challenge is not the achievement of higher benchmark accuracy but the construction of verifiable, contestable, and resource-proportionate sociotechnical systems. We conclude with a research agenda emphasizing hybrid architectures, uncertainty communication, cross-domain validation, and participatory governance as prerequisites for trustworthy deployment.
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