STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes

• URL: http://arxiv.org/abs/2508.10427v2
• Date: Tue, 14 Oct 2025 06:54:59 GMT
• Title: STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes
• Authors: Keishi Ishihara, Kento Sasaki, Tsubasa Takahashi, Daiki Shiono, Yu Yamaguchi,
• Abstract summary: STRIDE-QA is the largest visual question answering dataset fortemporal reasoning in urban driving.<n>It supports both object-centric and ego-centric reasoning through spatial localization and temporal prediction.<n>Our benchmarks demonstrate that existing Vision-Language Models (VLMs) struggle to achieve near-zero scores on prediction consistency.
• Score: 5.685235562999083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) have been applied to autonomous driving to support decision-making in complex real-world scenarios. However, their training on static, web-sourced image-text pairs fundamentally limits the precise spatiotemporal reasoning required to understand and predict dynamic traffic scenes. We address this critical gap with STRIDE-QA, a large-scale visual question answering (VQA) dataset for physically grounded reasoning from an ego-centric perspective. Constructed from 100 hours of multi-sensor driving data in Tokyo, capturing diverse and challenging conditions, STRIDE-QA is the largest VQA dataset for spatiotemporal reasoning in urban driving, offering 16 million QA pairs over 285K frames. Grounded by dense, automatically generated annotations including 3D bounding boxes, segmentation masks, and multi-object tracks, the dataset uniquely supports both object-centric and ego-centric reasoning through three novel QA tasks that require spatial localization and temporal prediction. Our benchmarks demonstrate that existing VLMs struggle significantly, achieving near-zero scores on prediction consistency. In contrast, VLMs fine-tuned on STRIDE-QA exhibit dramatic performance gains, achieving 55% success in spatial localization and 28% consistency in future motion prediction, compared to near-zero scores from general-purpose VLMs. Therefore, STRIDE-QA establishes a comprehensive foundation for developing more reliable VLMs for safety-critical autonomous systems.

Related papers

• From Indoor to Open World: Revealing the Spatial Reasoning Gap in MLLMs [65.04549036809557]
  We introduce a benchmark built from pedestrian-perspective videos captured with synchronized stereo cameras, LiDAR, and IMU/GPS sensors.<n>This dataset provides metrically precise 3D information, enabling the automatic generation of spatial reasoning questions.<n> Evaluations reveal that the performance gains observed in structured indoor benchmarks vanish in open-world settings.
  arXiv  Detail & Related papers  (2025-12-22T18:58:12Z)
• Box-QAymo: Box-Referring VQA Dataset for Autonomous Driving [27.39309272688527]
  Interpretable communication is essential for safe and trustworthy autonomous driving.<n>Current vision-language models (VLMs) often operate under idealized assumptions and struggle to capture user intent in real-world scenarios.<n>Box-QAymo is a box-referring dataset and benchmark designed to evaluate robustness and finetune VLMs on spatial and temporal reasoning over user-specified objects.
  arXiv  Detail & Related papers  (2025-07-01T07:40:16Z)
• STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving [16.602141801221364]
  STSBench is a framework to benchmark holistic understanding of vision-language models (VLMs) for autonomous driving.<n>The benchmark features 43 diverse scenarios spanning multiple views, resulting in 971 human-verified multiple-choice questions.<n>A thorough evaluation uncovers shortcomings in existing models' ability to reason about fundamental traffic dynamics in complex environments.
  arXiv  Detail & Related papers  (2025-06-06T16:25:22Z)
• Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames [17.975173937253494]
  An embodied AI assistant operating on egocentric video must integrate spatial cues across time.<n>Disjoint-3DQA is a generative QA benchmark that evaluates this ability of VLMs.
  arXiv  Detail & Related papers  (2025-05-30T06:32:26Z)
• NuScenes-SpatialQA: A Spatial Understanding and Reasoning Benchmark for Vision-Language Models in Autonomous Driving [10.41584658117874]
  We propose NuScenes-SpatialQA, the first large-scale ground-truth-based Question-Answer (QA) benchmark designed to evaluate the spatial understanding and reasoning capabilities of Vision-Language Models (VLMs) in autonomous driving.<n>Built upon the NuScenes dataset, the benchmark is constructed through an automated 3D scene graph generation pipeline and a QA generation pipeline.<n>Using this benchmark, we conduct extensive experiments on diverse VLMs, including both general and spatial-enhanced models, providing the first comprehensive evaluation of their spatial capabilities in autonomous driving.
  arXiv  Detail & Related papers  (2025-04-04T04:43:10Z)
• AutoDrive-QA: A Multiple-Choice Benchmark for Vision-Language Evaluation in Urban Autonomous Driving [0.7734726150561086]
  We introduce AutoDrive-QA, the first benchmark that systematically converts open-ended driving QA into structured multiple-choice questions.<n>We show that fine-tuning LLaVA-1.5-7B improves accuracy by about six percentage points across tasks, GPT-4V achieves the strongest zero-shot performance with up to 69.8% accuracy, and Qwen2-VL models also perform competitively.
  arXiv  Detail & Related papers  (2025-03-20T01:32:00Z)
• DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding [76.3876070043663]
  We propose DriveLMM-o1, a dataset and benchmark designed to advance step-wise visual reasoning for autonomous driving.<n>Our benchmark features over 18k VQA examples in the training set and more than 4k in the test set, covering diverse questions on perception, prediction, and planning.<n>Our model achieves a +7.49% gain in final answer accuracy, along with a 3.62% improvement in reasoning score over the previous best open-source model.
  arXiv  Detail & Related papers  (2025-03-13T17:59:01Z)
• RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments [62.5830455357187]
  We setup an egocentric multi-sensor data collection platform based on 3 main types of sensors (Camera, LiDAR and Fisheye)<n>A large-scale multimodal dataset is constructed, named RoboSense, to facilitate egocentric robot perception.
  arXiv  Detail & Related papers  (2024-08-28T03:17:40Z)
• SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities [59.39858959066982]
  understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. We develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA.
  arXiv  Detail & Related papers  (2024-01-22T18:01:01Z)
• NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario [77.14723238359318]
  NuScenesQA is the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. We leverage existing 3D detection annotations to generate scene graphs and design question templates manually. We develop a series of baselines that employ advanced 3D detection and VQA techniques.
  arXiv  Detail & Related papers  (2023-05-24T07:40:50Z)
• Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
  In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. We show competitive detection and attribute recognition results, as well as a more stable MTL training.
  arXiv  Detail & Related papers  (2020-12-04T15:10:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.