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.

翻译：交通事故常导致致命伤害，截至2023年已造成超过5000万人死亡。为降低驾驶风险并确保人身安全，帮助车辆在行进过程中预测重要目标至关重要。以往关于重要目标检测的研究主要评估个体参与者的重要性，将其视为独立实体，却常忽略这些参与者之间的关联。遗憾的是，这种方法在复杂场景中检测重要目标的效果欠佳。为此，我们提出驾驶场景关系自理解Transformer（DRUformer），旨在增强重要目标检测任务。DRUformer是一种基于Transformer的多模态重要目标检测模型，综合考虑驾驶场景中所有参与者之间的关系。鉴于驾驶意图也显著影响驾驶过程中的重要目标检测，我们嵌入了驾驶意图编码模块。为评估方法性能，我们在DRAMA数据集上开展了对比实验，将我们的模型与其他现有最优（SOTA）模型进行较量。结果表明，与SOTA方法相比，mIoU提升16.2%，ACC提升12.3%。此外，我们定性分析了模型在不同道路场景和类别中检测重要目标的能力，凸显其在多样化情境下的有效性。最后，我们进行了多项消融实验，以评估DRUformer模型中各模块的效率。