DRUformer：通过驾驶关系自理解增强驾驶场景重要目标检测
DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding
AI总结：
针对现有驾驶场景重要目标检测忽略参与者关联的问题，提出融合驾驶意图与参与者关系的多模态Transformer模型DRUformer，在DRAMA数据集上mIoU和ACC较SOTA分别提升16.2%、12.3%。
摘要：
交通事故常导致致命伤害，截至2023年已造成超5000万人死亡。为降低驾驶风险、保障人身安全，辅助车辆在行驶过程中预判重要目标至关重要。以往的重要目标检测研究主要评估单个交通参与者的重要性，将其视为独立实体，往往忽略了这些参与者之间的关联。遗憾的是，这种方法在复杂场景下的重要目标检测效果较差。针对这一问题，我们提出了驾驶场景关系自理解Transformer（DRUformer），旨在提升重要目标检测任务的性能。DRUformer是一种基于Transformer的多模态重要目标检测模型，会考虑驾驶场景中所有参与者之间的关系。鉴于驾驶意图也会显著影响驾驶过程中的重要目标检测，我们加入了驾驶意图嵌入模块。为评估所提方法的性能，我们在DRAMA数据集上开展了对比实验，将该模型与其他前沿（SOTA）模型进行比较。结果显示，与SOTA方法相比，该模型的mIoU显著提升16.2%，ACC大幅提高12.3%。此外，我们对模型在不同道路场景和类别下的重要目标检测能力进行了定性分析，凸显了其在多样场景下的有效性。最后，我们开展了多项消融实验，以评估DRUformer模型中所提模块的效能。
英文摘要：
Traffic accidents frequently lead to fatal injuries, contributing to over 50 million deaths until 2023. To mitigate driving hazards and ensure personal safety, it is crucial to assist vehicles in anticipating important objects during travel. Previous research on important object detection primarily assessed the importance of individual participants, treating them as independent entities and frequently overlooking the connections between these participants. Unfortunately, this approach has proven less effective in detecting important objects in complex scenarios. In response, we introduce Driving scene Relationship self-Understanding transformer (DRUformer), designed to enhance the important object detection task. The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario. Recognizing that driving intention also significantly affects the detection of important objects during driving, we have incorporated a module for embedding driving intention. To assess the performance of our approach, we conducted a comparative experiment on the DRAMA dataset, pitting our model against other state-of-the-art (SOTA) models. The results demonstrated a noteworthy 16.2\% improvement in mIoU and a substantial 12.3\% boost in ACC compared to SOTA methods. Furthermore, we conducted a qualitative analysis of our model's ability to detect important objects across different road scenarios and classes, highlighting its effectiveness in diverse contexts. Finally, we conducted various ablation studies to assess the efficiency of the proposed modules in our DRUformer model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
