Background:The environment that individuals are surrounded by have been linked to have an effect on affect,like anxiety,and well-being.On a whole,rural and natural environment scenes have been showed through previous ...Background:The environment that individuals are surrounded by have been linked to have an effect on affect,like anxiety,and well-being.On a whole,rural and natural environment scenes have been showed through previous research to increase positive affect and well-being.Until now,the methods of assessing affect in relation to environmental scene perception have been studied in a healthy sample,and mostly via self-report questionnaires and heart rate.Here,we present a novel quantitative research study that uses frontal electroencephalography(EEG)asymmetry to investigate the impact of viewing images of environmental scenes on affect in a sample of self-reported sub-clinically anxious adults.Frontal EEG asymmetry has previously been used in research related to motivation and assessing emotional affect,with most researchers showing greater left-frontal hemisphere activity compared to the right being associated with positive affect and approach behaviours.Consequently,frontal asymmetry EEG can be used to explore the impact of scene perception on affect.Methods:Forty-six participants(18-52 years)took part in the study.To determine the psychophysiological predictors of affect,specifically anxiety,we monitored brain activity using EEG,while participants viewed a series of natural and man-made images on a computer screen.Natural images consisted of beaches,forests,meadows,mountains,waterfalls.Man-made images consisted of cityscapes,construction sites,highways,skyscrapers and street views.EEG was Fourier transformed,and the alpha-band frequencies(8-12 Hz)isolated and averaged across each image type.Results:Preliminary analysis of frontal-asymmetry shows that individuals with sub-clinical levels of anxiety experience significantly more negative affect(i.e.,increased right asymmetry in alpha bands,(M=−3.15,SD=0.63)when viewing man-made images compared to control participants(M=−1.02,SD=0.67).These preliminary results contrast to when viewing natural images,whereby both controls and the anxious individuals experience high levels of positive affect(i.e.,increased left asymmetry in alpha bands:(Manxiety=3.31,SDanxiety=2.26;Mcontrol=3.33,SDcontrol=1.12).Lastly,frontal-asymmetry indices were significantly different(t=17.48,P<0.001,d=2.58,BF10=3.81e+18)when viewing natural and man-made images.This result was consistent across both groups.Conclusions:This research presents a novel approach to investigating the neuro-cognitive correlates of affect and scene perception.Additionally,these initial observations would indicate that man-made scenes induce negative affect,and that this effect is amplified in individuals with sub-clinical levels of anxiety.Future work should expand this research to investigate environmental scene perception in individuals with clinical levels of anxiety,and use other physiological measures,such as heart-rate variability and eye-tracking to objectively assess affect.展开更多
Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real...Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.展开更多
Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges...Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges mentioned above with a single model.To tackle this dilemma,this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting(STSIGMA),an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework.ST-SIGMA adopts a trident encoder-decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view(BEV)maps simultaneously.Specifically,an iterative aggregation network is first employed as the scene semantic encoder(SSE)to learn diverse scene information.To preserve dynamic interactions of traffic agents,ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction encoder.Meanwhile,a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed.Extensive experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-theart(SOTA)methods in terms of scene perception and trajectory forecasting,respectively.Therefore,the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in realworld AD scenarios.展开更多
With the support of edge computing,the synergy and collaboration among central cloud,edge cloud,and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture.This integration ...With the support of edge computing,the synergy and collaboration among central cloud,edge cloud,and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture.This integration unlocks the value of data and computational power,presenting significant opportunities for large-scale 3D scene modeling and XR presentation.In this paper,we explore the perspectives and highlight new challenges in 3D scene modeling and XR presentation based on point cloud within the cloud-edge-client integrated architecture.We also propose a novel cloud-edge-client integrated technology framework and a demonstration of municipal governance application to address these challenges.展开更多
A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain usin...A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.展开更多
We present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments.While recent factor graph based state optimization algo...We present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments.While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers,their dynamic motions are rarely considered.In this paper,we exploit the consensus of 3 D motions for landmarks extracted from the same rigid body for clustering,and to identify static and dynamic objects in a unified manner.Specifically,our algorithm builds a noise-aware motion affinity matrix from landmarks,and uses agglomerative clustering to distinguish rigid bodies.Using decoupled factor graph optimization to revise their shapes and trajectories,we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally.Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach,and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.展开更多
文摘Background:The environment that individuals are surrounded by have been linked to have an effect on affect,like anxiety,and well-being.On a whole,rural and natural environment scenes have been showed through previous research to increase positive affect and well-being.Until now,the methods of assessing affect in relation to environmental scene perception have been studied in a healthy sample,and mostly via self-report questionnaires and heart rate.Here,we present a novel quantitative research study that uses frontal electroencephalography(EEG)asymmetry to investigate the impact of viewing images of environmental scenes on affect in a sample of self-reported sub-clinically anxious adults.Frontal EEG asymmetry has previously been used in research related to motivation and assessing emotional affect,with most researchers showing greater left-frontal hemisphere activity compared to the right being associated with positive affect and approach behaviours.Consequently,frontal asymmetry EEG can be used to explore the impact of scene perception on affect.Methods:Forty-six participants(18-52 years)took part in the study.To determine the psychophysiological predictors of affect,specifically anxiety,we monitored brain activity using EEG,while participants viewed a series of natural and man-made images on a computer screen.Natural images consisted of beaches,forests,meadows,mountains,waterfalls.Man-made images consisted of cityscapes,construction sites,highways,skyscrapers and street views.EEG was Fourier transformed,and the alpha-band frequencies(8-12 Hz)isolated and averaged across each image type.Results:Preliminary analysis of frontal-asymmetry shows that individuals with sub-clinical levels of anxiety experience significantly more negative affect(i.e.,increased right asymmetry in alpha bands,(M=−3.15,SD=0.63)when viewing man-made images compared to control participants(M=−1.02,SD=0.67).These preliminary results contrast to when viewing natural images,whereby both controls and the anxious individuals experience high levels of positive affect(i.e.,increased left asymmetry in alpha bands:(Manxiety=3.31,SDanxiety=2.26;Mcontrol=3.33,SDcontrol=1.12).Lastly,frontal-asymmetry indices were significantly different(t=17.48,P<0.001,d=2.58,BF10=3.81e+18)when viewing natural and man-made images.This result was consistent across both groups.Conclusions:This research presents a novel approach to investigating the neuro-cognitive correlates of affect and scene perception.Additionally,these initial observations would indicate that man-made scenes induce negative affect,and that this effect is amplified in individuals with sub-clinical levels of anxiety.Future work should expand this research to investigate environmental scene perception in individuals with clinical levels of anxiety,and use other physiological measures,such as heart-rate variability and eye-tracking to objectively assess affect.
文摘Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.
基金Basic and Advanced Research Projects of CSTC,Grant/Award Number:cstc2019jcyj-zdxmX0008Science and Technology Research Program of Chongqing Municipal Education Commission,Grant/Award Numbers:KJQN202100634,KJZDK201900605National Natural Science Foundation of China,Grant/Award Number:62006065。
文摘Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges mentioned above with a single model.To tackle this dilemma,this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting(STSIGMA),an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework.ST-SIGMA adopts a trident encoder-decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view(BEV)maps simultaneously.Specifically,an iterative aggregation network is first employed as the scene semantic encoder(SSE)to learn diverse scene information.To preserve dynamic interactions of traffic agents,ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction encoder.Meanwhile,a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed.Extensive experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-theart(SOTA)methods in terms of scene perception and trajectory forecasting,respectively.Therefore,the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in realworld AD scenarios.
基金the National Natural Science Foundation of China(U22B2034)the Fundamental Research Funds for the Central Universities(226-2022-00064).
文摘With the support of edge computing,the synergy and collaboration among central cloud,edge cloud,and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture.This integration unlocks the value of data and computational power,presenting significant opportunities for large-scale 3D scene modeling and XR presentation.In this paper,we explore the perspectives and highlight new challenges in 3D scene modeling and XR presentation based on point cloud within the cloud-edge-client integrated architecture.We also propose a novel cloud-edge-client integrated technology framework and a demonstration of municipal governance application to address these challenges.
基金supported by the National Natural Science Foundation of China(No.61405191)the Jilin Province Science Foundation for Youths of China(No.20150520102JH)
文摘A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.
基金supported by the National Key Technology R&D Program(Project No.2017YFB1002604)the Joint NSFC-DFG Research Program(Project No.61761136018)the National Natural Science Foundation of China(Project No.61521002)。
文摘We present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments.While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers,their dynamic motions are rarely considered.In this paper,we exploit the consensus of 3 D motions for landmarks extracted from the same rigid body for clustering,and to identify static and dynamic objects in a unified manner.Specifically,our algorithm builds a noise-aware motion affinity matrix from landmarks,and uses agglomerative clustering to distinguish rigid bodies.Using decoupled factor graph optimization to revise their shapes and trajectories,we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally.Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach,and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.
