Vega: Learning to Drive with Natural Language Instructions
This addresses the need for more flexible and personalized autonomous driving systems, though it is incremental as it builds on existing vision-language-action models.
The paper tackles the problem of enabling autonomous driving systems to follow diverse natural language instructions for personalized driving, by constructing a large-scale dataset (InstructScene) with 100,000 scenes and proposing a unified model (Vega) that achieves superior planning performance and strong instruction-following abilities.
Vision-language-action models have reshaped autonomous driving to incorporate languages into the decision-making process. However, most existing pipelines only utilize the language modality for scene descriptions or reasoning and lack the flexibility to follow diverse user instructions for personalized driving. To address this, we first construct a large-scale driving dataset (InstructScene) containing around 100,000 scenes annotated with diverse driving instructions with the corresponding trajectories. We then propose a unified Vision-Language-World-Action model, Vega, for instruction-based generation and planning. We employ the autoregressive paradigm to process visual inputs (vision) and language instructions (language) and the diffusion paradigm to generate future predictions (world modeling) and trajectories (action). We perform joint attention to enable interactions between the modalities and use individual projection layers for different modalities for more capabilities. Extensive experiments demonstrate that our method not only achieves superior planning performance but also exhibits strong instruction-following abilities, paving the way for more intelligent and personalized driving systems.