One of the most important tasks for ensuring safe autonomous driving systems is accurately detecting road traffic lights and accurately determining how they impact the driver's actions. In various real-world driving situations, a scene may have numerous traffic lights with varying levels of relevance to the driver, and thus, distinguishing and detecting the lights that are relevant to the driver and influence the driver's actions is a critical safety task. This paper proposes a traffic light detection model which focuses on this task by first defining salient lights as the lights that affect the driver's future decisions. We then use this salience property to construct the LAVA Salient Lights Dataset, the first US traffic light dataset with an annotated salience property. Subsequently, we train a Deformable DETR object detection transformer model using Salience-Sensitive Focal Loss to emphasize stronger performance on salient traffic lights, showing that a model trained with this loss function has stronger recall than one trained without.

翻译：确保自动驾驶系统安全运行的最关键任务之一是准确检测道路交通灯，并精确判断其对驾驶员行为的影响。在实际驾驶场景中，视野中可能出现多个交通灯，这些灯与驾驶员的关联程度各不相同。因此，区分并检测与驾驶员相关且影响驾驶员操作的交通灯是一项至关重要的安全任务。本文提出了一种交通灯检测模型，通过首先定义显著性交通灯（即影响驾驶员未来决策的灯）来聚焦这一任务。随后，我们利用这一显著性属性构建了LAVA显著性交通灯数据集——美国首个包含显著性标注属性的交通灯数据集。在此基础上，我们使用显著性敏感焦点损失函数训练Deformable DETR目标检测变换器模型，以增强对显著性交通灯的检测性能。实验表明，采用该损失函数训练的模型在召回率上显著优于未使用该损失函数的模型。