论文标题
查看，参加和制动：基于注意的显着图图预测模型，用于端到端驾驶
See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving
论文作者
论文摘要
视觉感知是驱动决策的最关键输入。在这项研究中，我们的目的是了解显着性和驱动决策之间的关系。我们提出了一种基于注意力的显着性图预测模型，用于制定制动决策，这种方法为驾驶任务构建了整体模型，并且可以扩展到其他驾驶决策（例如转向和加速）。提出的模型是一个深神网络模型，该模型将输入图像从具有注意机制的复发性神经网络中馈入提取的特征。然后预测的显着图用于做出制动决定。我们使用驱动注意数据集BDD-A和显着数据集CAT2000进行了培训和评估。
Visual perception is the most critical input for driving decisions. In this study, our aim is to understand relationship between saliency and driving decisions. We present a novel attention-based saliency map prediction model for making braking decisions This approach constructs a holistic model to the driving task and can be extended for other driving decisions like steering and acceleration. The proposed model is a deep neural network model that feeds extracted features from input image to a recurrent neural network with an attention mechanism. Then predicted saliency map is used to make braking decision. We trained and evaluated using driving attention dataset BDD-A, and saliency dataset CAT2000.
