Driving rule extraction based on cognitive behavior analysis

Zhao, Yucheng; Liang, Jun; Chen, Long; Cai, Yingfeng; Yao, Ming; Guo-dong, Hua; Zhu, Ning

doi:10.1007/s11771-020-4286-1

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Wang et al presented a crash prediction method based on vehicle trajectory data extracted from intersection videos collected in Fengxian, China by an unmanned aerial vehicle [5]. Zhao et al proposed a driving behaviour rule extraction algorithm based on the driver's long-term driving experience in the processes of perception, interaction and vehicle control of road traffic information [6]. Considering travel time, travel time reliability and distance, Sun et al proposed a multi-criteria user equilibrium model [7].…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Vehicle Tracking Algorithms for Extracting Integral Trajectories from Consecutive Videos

Chen

Wang

DONG

2022

PROMTT

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This study introduces a novel methodological frame-work for extracting integral vehicle trajectories from several consecutive pictures automatically. The frame-work contains camera observation, eliminating image distortions, video stabilising, stitching images, identify-ing vehicles and tracking vehicles. Observation videos of four sections in South Fengtai Road, Nanjing, Jiangsu Province, China are taken as a case study to validate the framework. As key points, six typical tracking algorithms, including boosting, CSRT, KCF, median flow, MIL and MOSSE, are compared in terms of tracking reliability, operational time, random access memory (RAM) usage and data accuracy. Main impact factors taken into con-sideration involve vehicle colours, zebra lines, lane lines, lamps, guide boards and image stitching seams. Based on empirical analysis, it is found that MOSSE requires the least operational time and RAM usage, whereas CSRT presents the best tracking reliability. In addition, all tracking algorithms produce reliable vehicle trajecto-ry and speed data if vehicles are tracked steadily.

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Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Vehicle Tracking Algorithms for Extracting Integral Trajectories from Consecutive Videos

Chen

Wang

DONG

2022

PROMTT

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“…For VR technology, the simulation of the man-machine layout design of a VR system is constrained by man-machine characteristics, such as human physiology, psychology, and cognition [4,5]. According to the needs of users, designers rearrange the combination of design elements to optimize the VR system interface, thus improving user comfort and work efficiency [6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Virtual Reality Visualization Interface Based on Kansei Engineering Image Space Research

Tang

et al. 2020

Symmetry

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To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system’s human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users’ perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user’s perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user’s perceptual imagery index, and thus the user’s task operation experience was better.

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Effects of Driver Response Time Under Take-Over Control Based on CAR-ToC Model in Human–Machine Mixed Traffic Flow

Zhao

Geng²,

Liang³

et al. 2023

Automot. Innov.

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Driving rule extraction based on cognitive behavior analysis

Cited by 5 publications

References 35 publications

Empirical Analysis of Vehicle Tracking Algorithms for Extracting Integral Trajectories from Consecutive Videos

Empirical Analysis of Vehicle Tracking Algorithms for Extracting Integral Trajectories from Consecutive Videos

Optimal Design of Virtual Reality Visualization Interface Based on Kansei Engineering Image Space Research

Effects of Driver Response Time Under Take-Over Control Based on CAR-ToC Model in Human–Machine Mixed Traffic Flow

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