基于领域对抗循环神经网络的鲁棒和主体无关驾驶机动预测
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks
摘要：
通过深度学习和计算机视觉技术，可以提前几秒钟准确预测驾驶操作。尽管使学习模型适应新驾驶员和不同车辆是强大的驾驶员辅助系统的关键，但到目前为止，这个问题几乎没有受到关注。这项工作提出通过领域自适应，一种与迁移学习密切相关的技术来应对这一挑战。提出了将域对抗递归神经网络（DA-RNN）应用于多模态时间序列驱动数据的概念证明，其中通过最大化辅助域分类器的损失来学习域不变特征。我们的实现使用Brain 4Cars数据集的单个驱动程序的leave-one-driver-out方法进行评估，以及使用通过驾驶模拟获得的新数据集，与没有适应相比，性能分别平均提高了30%和114%。我们还展示了微调网络部分以优化域独立特征提取的重要性。结果表明，该方法的适用性，驾驶辅助系统以及培训和仿真环境。
英文摘要：
Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to tackle this challenge through domain adaptation, a technique closely related to transfer learning. A proof of concept for the application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to multi-modal time series driving data is presented, in which domain-invariant features are learned by maximizing the loss of an auxiliary domain classifier. Our implementation is evaluated using a leave-one-driver-out approach on individual drivers from the Brain4Cars dataset, as well as using a new dataset acquired through driving simulations, yielding an average increase in performance of 30% and 114% respectively compared to no adaptation. We also show the importance of fine-tuning sections of the network to optimise the extraction of domain-independent features. The results demonstrate the applicability of the approach to driver-assistance systems as well as training and simulation environments.
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。
