Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developmen...Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.展开更多
Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their texture.No method has previously been developed to...Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their texture.No method has previously been developed to directly explore the relationship between foggy images and semantic segmentation images.We investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation GAN.The edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation GAN.The semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation images.Experiments on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.展开更多
基金supported by the National Natural Science Foundation of China(61403158,61520106008)the Project of the Education Department of Jilin Province(2016-429)
文摘Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.
基金supported in part by the National Key Research and Development Program of China(2018YFB1305002)the National Natural Science Foundation of China(62006256)+2 种基金the Postdoctoral Science Foundation of China(2020M683050)the Key Research and Development Program of Guangzhou(202007050002)the Fundamental Research Funds for the Central Universities(67000-31610134)。
文摘Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their texture.No method has previously been developed to directly explore the relationship between foggy images and semantic segmentation images.We investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation GAN.The edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation GAN.The semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation images.Experiments on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.
