LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset
Authors: name: Royden Wagner, name: Omer Sahin Tas, name: Jaime Villa, name: Felix Hauser, name: Yinzhe Shen, name: Marlon Steiner, name: Dominik Strutz, name: Carlos Fernandez, name: Christian Kinzig, name: Guillermo S. Guitierrez-Cabello, name: Hendrik Königshof, name: Fabian Immel, name: Richard Schwarzkopf, name: Nils Alexander Rack, name: Kevin Rösch, name: Kaiwen Wang, name: Jan-Hendrik Pauls, name: Martin Lauer, name: Igor Gilitschenski, name: Holger Caesar, name: Christoph Stiller
Organization: Karlsruhe Institute of Technology

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