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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.23607 |
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| _version_ | 1866918430157832192 |
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| author | Wagner, Royden Tas, Omer Sahin Villa, Jaime Hauser, Felix Shen, Yinzhe Steiner, Marlon Strutz, Dominik Fernandez, Carlos Kinzig, Christian Guitierrez-Cabello, Guillermo S. Königshof, Hendrik Immel, Fabian Schwarzkopf, Richard Rack, Nils Alexander Rösch, Kevin Wang, Kaiwen Pauls, Jan-Hendrik Lauer, Martin Gilitschenski, Igor Caesar, Holger Stiller, Christoph |
| author_facet | Wagner, Royden Tas, Omer Sahin Villa, Jaime Hauser, Felix Shen, Yinzhe Steiner, Marlon Strutz, Dominik Fernandez, Carlos Kinzig, Christian Guitierrez-Cabello, Guillermo S. Königshof, Hendrik Immel, Fabian Schwarzkopf, Richard Rack, Nils Alexander Rösch, Kevin Wang, Kaiwen Pauls, Jan-Hendrik Lauer, Martin Gilitschenski, Igor Caesar, Holger Stiller, Christoph |
| contents | In real-world domains such as self-driving, generalization to rare scenarios remains a fundamental challenge. To address this, we introduce a new dataset designed for end-to-end driving that focuses on long-tail driving events. We provide multi-view video data, trajectories, high-level instructions, and detailed reasoning traces, facilitating in-context learning and few-shot generalization. The resulting benchmark for multimodal models, such as VLMs and VLAs, goes beyond safety and comfort metrics by evaluating instruction following and semantic coherence between model outputs. The multilingual reasoning traces in English, Spanish, and Chinese are from domain experts with diverse cultural backgrounds. Thus, our dataset is a unique resource for studying how different forms of reasoning affect driving competence. Our dataset is available at: https://hf.co/datasets/kit-mrt/kitscenes-longtail |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23607 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset Wagner, Royden Tas, Omer Sahin Villa, Jaime Hauser, Felix Shen, Yinzhe Steiner, Marlon Strutz, Dominik Fernandez, Carlos Kinzig, Christian Guitierrez-Cabello, Guillermo S. Königshof, Hendrik Immel, Fabian Schwarzkopf, Richard Rack, Nils Alexander Rösch, Kevin Wang, Kaiwen Pauls, Jan-Hendrik Lauer, Martin Gilitschenski, Igor Caesar, Holger Stiller, Christoph Computer Vision and Pattern Recognition Robotics In real-world domains such as self-driving, generalization to rare scenarios remains a fundamental challenge. To address this, we introduce a new dataset designed for end-to-end driving that focuses on long-tail driving events. We provide multi-view video data, trajectories, high-level instructions, and detailed reasoning traces, facilitating in-context learning and few-shot generalization. The resulting benchmark for multimodal models, such as VLMs and VLAs, goes beyond safety and comfort metrics by evaluating instruction following and semantic coherence between model outputs. The multilingual reasoning traces in English, Spanish, and Chinese are from domain experts with diverse cultural backgrounds. Thus, our dataset is a unique resource for studying how different forms of reasoning affect driving competence. Our dataset is available at: https://hf.co/datasets/kit-mrt/kitscenes-longtail |
| title | LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2603.23607 |