To improve the classification performance of the kernel minimum squared error（ KMSE）, an enhanced KMSE algorithm（ EKMSE） is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix.Compared with the common methods, the newobjective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE,some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification（ CRC）.
本文提出了一种用于未来自动驾驶场景的虚拟车道技术,旨在突破当前自动驾驶行业的发展瓶颈,并为未来融合飞行汽车交通系统(Flying Car Transportation Systems,FCTS)的自动驾驶场景提供一种创新性技术方案.虚拟车道技术伴随自动驾驶等级的提升协同发展,从面向有人驾驶,到面向全智能驾驶,再到面向本文所提出的L6空地全域自动驾驶,从而实现空地一体化交通的愿景.本文结合了自动驾驶、数字孪生、物联网(Internet of Things,IoT)、人工智能(Artificial Intelligence,AI)等各领域的最新技术对虚拟车道技术在每个发展阶段的应用场景和具体实现方法进行了详细介绍以及可行性分析,对自动驾驶行业明晰未来总体发展趋势和关键技术导向具有开创式的启发意义.
