WOMD-Reasoning: A Large-Scale Language Dataset for Interaction and Driving Intentions Reasoning
<|reference_start|>WOMD-Reasoning: A Large-Scale Language Dataset for Interaction and Driving Intentions Reasoning: We propose Waymo Open Motion Dataset-Reasoning (WOMD-Reasoning), a language annotation dataset built on WOMD, with a focus on describing and reasoning interactions and intentions in driving scenarios. Previous language datasets primarily captured interactions caused by close distances. However, interactions induced by traffic rules and human intentions, which can occur over long distances, are yet sufficiently covered, despite being very common and more challenging for prediction or planning models to understand. Therefore, our WOMD-Reasoning focuses extensively on these interactions, providing a total of 409k Q&As for varying types of interactions. Additionally, WOMD-Reasoning presents by far the largest Q&A dataset on real-world driving scenarios, with around 3 million Q&As covering various topics of autonomous driving from map descriptions, motion status descriptions, to narratives and analyses of agents' interactions, behaviors, and intentions. This extensive textual information enables fine-tuning driving-related Large Language Models (LLMs) for a wide range of applications like scene description, prediction, planning, etc. By incorporating interaction and intention language from WOMD-Reasoning, we see significant enhancements in the performance of the state-of-the-art trajectory prediction model, Multipath++, with improvements of 10.14% in $MR_6$ and 6.90% in $minFDE_6$, proving the effectiveness of WOMD-Reasoning. We hope WOMD-Reasoning would empower LLMs in driving to offer better interaction understanding and behavioral reasoning. The dataset is available on https://waymo.com/open/download .<|reference_end|>
@article{li2024womd-reasoning:,
    title={WOMD-Reasoning: A Large-Scale Language Dataset for Interaction and
  Driving Intentions Reasoning},
    author={Yiheng Li, Chongjian Ge, Chenran Li, Chenfeng Xu, Masayoshi Tomizuka,
  Chen Tang, Mingyu Ding, Wei Zhan},
    journal={arXiv preprint arXiv:2407.04281},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04281},
    primaryClass={cs.RO}
}