doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigation
This addresses the problem of integrating human instructions into autonomous vehicle systems for improved human-vehicle collaboration, though it is incremental as it builds on existing dataset efforts.
The paper introduces doScenes, a dataset for autonomous driving that includes natural language instructions and referentiality tags to link human directives with vehicle motion, enabling context-aware planning in real-world scenarios.
Human-interactive robotic systems, particularly autonomous vehicles (AVs), must effectively integrate human instructions into their motion planning. This paper introduces doScenes, a novel dataset designed to facilitate research on human-vehicle instruction interactions, focusing on short-term directives that directly influence vehicle motion. By annotating multimodal sensor data with natural language instructions and referentiality tags, doScenes bridges the gap between instruction and driving response, enabling context-aware and adaptive planning. Unlike existing datasets that focus on ranking or scene-level reasoning, doScenes emphasizes actionable directives tied to static and dynamic scene objects. This framework addresses limitations in prior research, such as reliance on simulated data or predefined action sets, by supporting nuanced and flexible responses in real-world scenarios. This work lays the foundation for developing learning strategies that seamlessly integrate human instructions into autonomous systems, advancing safe and effective human-vehicle collaboration for vision-language navigation. We make our data publicly available at https://www.github.com/rossgreer/doScenes