LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset
This dataset addresses the problem of rare driving scenarios for self-driving AI researchers, providing a unique resource with multilingual reasoning traces, but it is incremental as it builds on existing dataset efforts.
The authors tackled the challenge of generalization to rare scenarios in self-driving by introducing the KITScenes LongTail dataset, which includes multi-view video data, trajectories, high-level instructions, and detailed reasoning traces to facilitate in-context learning and few-shot generalization for multimodal models.
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