Cooperative Visual Augmentation Algorithm of Intelligent Vehicle Based on Inter-Vehicle Image Fusion

Liu, Wei; Ma, Yun; Gao, Mingqiang; Duan, Shuaidong; Wei, Longsheng

doi:10.3390/app112411917

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…And a graph neural network was used to aggregated features from other ICVs. Liu et al [ 123 ] used the 3D projection model to obtain feature points among ICVs, and those feature points were used to estimate geometric transformation parameters. These parameters were utilized in depth mapping transformation to integrate feature information.…”

Section: Vehicle–infrastructure Cooperative Perception Methodsmentioning

confidence: 99%

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Wang,

Wu,

Liang

et al. 2024

Sensors

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Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle–infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle–infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

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Section: Vehicle–infrastructure Cooperative Perception Methodsmentioning

confidence: 99%

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Wang,

Wu,

Liang

et al. 2024

Sensors

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show abstract

“…Sridhar et al [ 25 ] used cooperative relative positioning and map merging [ 26 ] to realize the cooperative perception of vehicles with a common field of view by sharing the image perception information of adjacent vehicles with the host vehicle. Liu et al [ 27 ] used V2V communication technology, based on a 3D inter-vehicle projection model and selected feature point matching to estimate the geometric transformation parameters to achieve inter-vehicle image information fusion through deep-affine transformation. It effectively overcomes the problem of perceived blind spots in traffic jams, but it overlooks the impact of viewing angles, and, in some cases, there will be size deviations.…”

Section: Cooperative Perception Information Fusionmentioning

confidence: 99%

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

Cui

Zhang

Xiao

et al. 2022

Sensors

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Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image–point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology.

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“…Among them, the problem of anticollision between vehicles has been paid attention to earlier by academians and industry. At present, the research of intervehicle collision warning algorithms is mainly divided into perceptual experience-based algorithms [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms

Xiao

2022

Computational Intelligence and Neuroscience

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The age of algorithms is here, and it is really changing people’s lives. More and more ethical problems related to algorithms have attracted people’s attention, but the related ethical research is still far behind the research of algorithms. As more intelligent algorithms emerge in an endless stream, there will also be a lot of algorithmic ethical issues. On the other hand, with the continuous improvement of the development level of the automobile industry, people have a stronger demand for the safety and stability of modern transportation, and more and more autonomous driving technology has been promoted and applied in the market. At present, most of the studies on the longitudinal collision avoidance system of vehicles use collision warning or emergency braking to avoid collision. However, when the vehicle is in a special situation such as high speed and slippery road, emergency steering is more effective. In order to further improve the vehicle safety and ethical algorithm design points, this article revolves around vehicle lateral active collision avoidance control method research, the collision avoidance decision-making, and path planning and collision avoidance transverse vehicle longitudinal motion control is analyzed, and based on automated driving simulation experiment, the tests carried out to verify the designed control strategy. The experimental results show that the proposed method not only has a good effect of preventing automatic driving collision but also can meet the requirements of algorithm ethics. This research can effectively guide the research of algorithmic ethics in the field of autonomous driving and effectively reduce the occurrence of traffic accidents.

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Cooperative Visual Augmentation Algorithm of Intelligent Vehicle Based on Inter-Vehicle Image Fusion

Cited by 4 publications

References 23 publications

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms

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