论文标题
通过注意机制提高转向角度预测的准确性和鲁棒性
Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism
论文作者
论文摘要
在本文中，我们的重点是增强自动驾驶任务的转向角度预测。我们通过调查了两种广泛采用的深神经结构的静脉来启动我们的探索，即重新连接和inceptionnets。在两个家庭中，我们都会系统地评估各种模型大小，以了解它们对性能的影响。值得注意的是，我们的关键贡献在于将注意机制纳入增强转向角度预测的准确性和鲁棒性。通过引入注意，我们的模型可以选择性地专注于输入数据中的关键区域，从而改善预测结果。我们的发现表明，我们的注意力增强模型不仅在转向角度平方误差（MSE）方面实现了最先进的结果，而且还表现出增强的对抗性鲁棒性，从而解决了现实世界中的关键问题。例如，在我们对Kaggle SAP的实验和我们创建的公开数据集的实验中，注意力可能导致转向角度预测的误差降低超过6％，并提高模型鲁棒性高达56.09％。
In this paper, our focus is on enhancing steering angle prediction for autonomous driving tasks. We initiate our exploration by investigating two veins of widely adopted deep neural architectures, namely ResNets and InceptionNets. Within both families, we systematically evaluate various model sizes to understand their impact on performance. Notably, our key contribution lies in the incorporation of an attention mechanism to augment steering angle prediction accuracy and robustness. By introducing attention, our models gain the ability to selectively focus on crucial regions within the input data, leading to improved predictive outcomes. Our findings showcase that our attention-enhanced models not only achieve state-of-the-art results in terms of steering angle Mean Squared Error (MSE) but also exhibit enhanced adversarial robustness, addressing critical concerns in real-world deployment. For example, in our experiments on the Kaggle SAP and our created publicly available datasets, attention can lead to over 6% error reduction in steering angle prediction and boost model robustness by up to 56.09%.
