Autonomous driving paper index
VLAAD: Vision and Language Assistant for Autonomous Driving
One-line summary
To address this, we introduce a multi-modal instruction tuning dataset that facilitates language models in learning visual instructions across diverse driving scenarios.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
While interpretable decision-making is pivotal in au-tonomous driving, research integrating natural language models remains a relatively untapped. To address this, we introduce a multi-modal instruction tuning dataset that facilitates language models in learning visual instructions across diverse driving scenarios. This dataset encompasses three primary tasks: conversation, detailed description, and complex reasoning. Capitalizing on this dataset, we present a multi-modal LLM driving assistant named VLAAD. After fine-tuned from our instruction-following dataset, VLAAD demonstrates proficient interpretive capabilities across a spectrum of driving situations. We open our work, dataset, and model, to public on github. https://github. com/sungyeonparkk/vision-assistant-for-driving
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