-
公开(公告)号：US12117829B1
公开(公告)日：2024-10-15
申请号：US18505068
申请日：2023-11-08
Applicant: ZHEJIANG LAB
Inventor： Yuntao Liu , Yongdong Zhu , Zhifeng Zhao , Wei Hua , Qian Huang , Shuyuan Zhao , Daoxun Li , Zimian Wu
CPC classification number: G05D1/0022 , B60W60/00 , H04L67/12 , B60W2556/45
Abstract: The present disclosure discloses an autonomous vehicle remote control apparatus and a method based on heterogeneous networks. The apparatus comprises a vehicle information acquisition module, a first message sending module, a first message receiving module and a first remote control module. According to the present disclosure, the possibility of failure of remote control is avoided or greatly reduced by bypassing the area where the network quality does not support remote control when planning a vehicle path, heterogeneous network resources are reasonably utilized on the vehicle driving path, the real-time performance of obtaining vehicle-related information by a remote control terminal is improved, and the availability and reliability of remote control and the safety of vehicle driving are effectively enhanced.
-
公开(公告)号：US12020490B2
公开(公告)日：2024-06-25
申请号：US18493795
申请日：2023-10-24
Applicant: ZHEJIANG LAB
Inventor： Qian Huang , Yongdong Zhu , Zhifeng Zhao
CPC classification number: G06V20/58
Abstract: The present application discloses a method and a device for estimating the position of a networked vehicle based on independent non-uniform increment sampling. By mapping a laser radar point cloud to a spatiotemporal aligned image, independent non-uniform increment sampling is carried out on the mapping points falling in an advanced semantic constraint region of the image according to a point density of the depth interval where the mapping points are located, and the virtual mapping points generated by sampling are reversely mapped to the original point cloud space and merged with the original point cloud, and the combined point cloud is used to estimate the position of the networked vehicle based on a deep learning method, so as to solve the inaccurate position estimation problem of sheltered or remote networked vehicles due to the sparseness or missing of its own point cloud clusters.
-
公开(公告)号：US12106551B2
公开(公告)日：2024-10-01
申请号：US18493833
申请日：2023-10-25
Applicant: ZHEJIANG LAB
Inventor： Qian Huang , Zhifeng Zhao , Yongdong Zhu , Yuntao Liu
CPC classification number: G06V10/806 , G06T7/73 , G06V10/247 , G06V10/7715 , G06V10/82 , G06V20/58 , G06V20/70 , G06T2207/20084 , G06T2207/30261
Abstract: The present application discloses a visual enhancement method and a system based on fusion of spatially aligned features of multiple networked vehicles. The method utilizes the visual features of networked vehicles themselves and their surroundings within a certain range, and performs feature fusion after spatial alignment to realize visual expansion and enhancement. After receiving the compressed intermediate feature map of the networked vehicles in a certain range around, the decompressed intermediate feature map is subjected to affine transformation, and the transformed aligned feature map is subjected to feature fusion based on a designed multi-feature self-learning network, so as to realize the complementation and enhancement among features while removing redundant features. The fused intermediate features are used to detect the target obstacles from the perspective of the networked vehicle itself and partially or completely blocked, thus improving the safety of driving connected vehicles.
-
公开(公告)号：US11941979B2
公开(公告)日：2024-03-26
申请号：US18349980
申请日：2023-07-11
Applicant: ZHEJIANG LAB
Inventor： Qian Huang , Kan Wu , Yongdong Zhu , Zhifeng Zhao
CPC classification number: G08G1/083 , G08G1/0112 , G08G1/0125 , G08G1/0145 , G08G1/08
Abstract: The present application discloses a traffic light control method for an urban road network based on expected return estimation, which uses C-V2X wireless communication technology to obtain real-time information of all vehicles and traffic state in the road network from vehicle-mounted terminals, and adaptively and dynamically controls the phase transformation of the traffic light. According to the present application, the expected returns of keeping the current phase and executing phase switch are calculated by estimating the timely driving distance and the future driving distance of the passable vehicles in the next green light duration in combination with the proposed road priority traffic index. By comparing the expected returns of keeping the current phase or switching to other phases, the best phase is selected, so as to make as many passable vehicles travel farther as possible in the next green light duration. Therefore, the efficiency of traffic will be improved.
-
-
-
Search Results
4
records
(took 0.014 seconds)
