高速行驶的汽车在道路换道时需要保持安全的间距与车速,为保障汽车在换道过程安全行驶,提出一种基于动态空间转换法(Ego Dynamic Space Transform,EDST)与强化学习(Double Deep Q Network,DDQN)的多场景汽车避障预警算法。将单目深度预...高速行驶的汽车在道路换道时需要保持安全的间距与车速,为保障汽车在换道过程安全行驶,提出一种基于动态空间转换法(Ego Dynamic Space Transform,EDST)与强化学习(Double Deep Q Network,DDQN)的多场景汽车避障预警算法。将单目深度预估图作为汽车航点最佳时刻,采用DDQN算法检测图像输入并执行动作输出。由于车辆换道场景的复杂性,采用对抗学习法(Adversarial Discriminative Domain Adaptation,ADDA)处理目标场景数据,实现车辆不同场景下的换道操作。选择多种场景测试车辆避障模型性能,所提出的自适应模型在复杂双向车道场景碰撞次数最少为3次。同时,能够换道数量最多为42次,优于EDST、DDQN以及DDQN+EDST模型,满足智能汽车安全换道要求。研究内容为高速驾驶车辆紧急避险提供重要的技术参考。展开更多
Recently, data-driven methods, especially deep learning, outperform other methods for rolling elementbearing (REB) fault diagnosis. Nevertheless, most research work assumes that REB dataset is unbiased. Inthe real ind...Recently, data-driven methods, especially deep learning, outperform other methods for rolling elementbearing (REB) fault diagnosis. Nevertheless, most research work assumes that REB dataset is unbiased. Inthe real industry applications, the dataset bias exists with REB owing to varying REB working conditions andnoise interference. Recently proposed adversarial discriminative domain adaptation (ADDA) is an increasinglypopular incarnation to solve dataset bias problem. However, it mainly devotes to realizing domain alignments, andignores class-level alignments;it can cause degradation of classification performance. In this study, we proposea new REB fault diagnosis model based on improved ADDA to address dataset bias. The proposed diagnosismodel realizes domain- and class-level alignments in dataset bias scenario;it consists of two feature extractors,a domain discriminator, and two label classifiers. The feature extractors and domain discriminator are trainedin an adversarial manner to minimize the domain difference in feature extractors. The domain discrepancy inlabel classifier is reduced by minimizing correlation alignment (CORAL) loss. We evaluate the proposed model onthe Case Western Reserve University (CWRU) bearing dataset and Paderborn University bearing dataset. Theproposed method yields better results than other methods and has good prospects for industrial applications.展开更多
文摘高速行驶的汽车在道路换道时需要保持安全的间距与车速,为保障汽车在换道过程安全行驶,提出一种基于动态空间转换法(Ego Dynamic Space Transform,EDST)与强化学习(Double Deep Q Network,DDQN)的多场景汽车避障预警算法。将单目深度预估图作为汽车航点最佳时刻,采用DDQN算法检测图像输入并执行动作输出。由于车辆换道场景的复杂性,采用对抗学习法(Adversarial Discriminative Domain Adaptation,ADDA)处理目标场景数据,实现车辆不同场景下的换道操作。选择多种场景测试车辆避障模型性能,所提出的自适应模型在复杂双向车道场景碰撞次数最少为3次。同时,能够换道数量最多为42次,优于EDST、DDQN以及DDQN+EDST模型,满足智能汽车安全换道要求。研究内容为高速驾驶车辆紧急避险提供重要的技术参考。
基金Foundation item:the Research on Intelligent Ship Testing and Verification(No.[2018]473)。
文摘Recently, data-driven methods, especially deep learning, outperform other methods for rolling elementbearing (REB) fault diagnosis. Nevertheless, most research work assumes that REB dataset is unbiased. Inthe real industry applications, the dataset bias exists with REB owing to varying REB working conditions andnoise interference. Recently proposed adversarial discriminative domain adaptation (ADDA) is an increasinglypopular incarnation to solve dataset bias problem. However, it mainly devotes to realizing domain alignments, andignores class-level alignments;it can cause degradation of classification performance. In this study, we proposea new REB fault diagnosis model based on improved ADDA to address dataset bias. The proposed diagnosismodel realizes domain- and class-level alignments in dataset bias scenario;it consists of two feature extractors,a domain discriminator, and two label classifiers. The feature extractors and domain discriminator are trainedin an adversarial manner to minimize the domain difference in feature extractors. The domain discrepancy inlabel classifier is reduced by minimizing correlation alignment (CORAL) loss. We evaluate the proposed model onthe Case Western Reserve University (CWRU) bearing dataset and Paderborn University bearing dataset. Theproposed method yields better results than other methods and has good prospects for industrial applications.
