Human Model-Based Active Driving System in Vehicular Dynamic Simulation

Kimpara, Hideyuki; Mbanisi, Kenechukwu C.; Fu, Jie; Li, Zhi; Prokhorov, Danil; Gennert, Michael A.

doi:10.1109/tits.2019.2906294

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…e simulated vehicle dynamics and vehicle handling are comparable to previously published experimental data on car following. However, at present, it is more difficult to realize all aspects [2]. It is of great significance to obtain the accurate position of the vehicle in the intelligent transportation system for improving active safety and realizing autonomous driving.…”

Section: Related Workmentioning

confidence: 99%

Key Technologies of Vehicle Active Safety System Based on Computer Vision

Qian

Wang

2022

Journal of Advanced Transportation

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Due to the increase in the number of urban vehicles and the irregular driving behavior of drivers, urban accidents frequently occur, causing serious casualties and economic losses. Active vehicle safety systems can monitor vehicle status and driver status online in real time. Computer vision technology simulates biological vision and can analyze, identify, detect, and track the data and information in the captured images. In terms of driving accident warning and vehicle status warning, the vehicle active safety system has the potential to enhance the driver’s ability to detect abnormal situations, prolong the processing time, and reduce the risk of safety accidents. In this paper, an active safety system is developed according to the existing vehicle electronic system framework, and the early warning decision is made by evaluating the relationship between the minimum early warning distance and the actual vehicle distance, speed, and other factors. In this paper, the kinematics model established by the vehicle active safety early warning system is designed. The results found that, within 400 ms of the driver’s judgment time, for the driver with the reaction time of 0.6 s and 0.9 s, the following distance of 20 m does not constitute a safety threat and no braking operation is required.

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Section: Related Workmentioning

confidence: 99%

Key Technologies of Vehicle Active Safety System Based on Computer Vision

Qian

Wang

2022

Journal of Advanced Transportation

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“…In recent years, a model-based approach has gained importance for efficient and high-quality product development (e.g. [32]), and for future model-based development, accumulating knowledge on the brain mechanism for the input-output relationship associated with driving will be useful.…”

Section: B Brain Activity Correlated With Viscoelastic Conditionsmentioning

confidence: 99%

Is Human Brain Activity During Driving Operations Modulated by the Viscoelastic Characteristics of a Steering Wheel?: An fMRI Study

et al. 2020

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To date, a neuroscientific investigation of drivers' steering behavior has never been performed because the reaction forces generated by the mechanical characteristics of a steering wheel have been difficult to assess in a magnetic resonance imaging (MRI) environment. In this study, using our previously developed MRI-compatible unit for steering reaction force generation, we investigated changes in human brain activity induced by varying the viscoelastic characteristics associated with manipulating a car steering wheel. Participants performed a simulated driving task with three levels of stiffness and viscosity. An amplitude effect of reaction forces on the measured brain activity due to varying stiffness was found in the primary motor cortex (M1) associated with hand representation. Conversely, the changes in the brain activity induced by varying viscosity were found more dorsally in the premotor cortex and the M1 than in regions associated with hand representation. These results are the first to demonstrate that various viscoelastic characteristics activate different motor regions; more specifically, stiffness and viscosity of the steering wheel mainly affected the motor control of the distal and proximal muscles, respectively.

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“…Kimpara et al proposed an active driving system based on the human body model with the cooperation of team members. The system adjusts the dynamics of the human body model so that the human body model can make a real response to the maneuver [ 9 ]. Han et al proposed the sequence view of sequence tags as a new deep learning model based on RNNs' encoding and decoding structure.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Liu

Ren

Qin³

2021

Computational Intelligence and Neuroscience

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In real life, people’s life gradually tends to be simple, so the convenience of online shopping makes more and more research begin to explore the convenience optimization of shopping, in which the fitting system is the research product. However, due to the immaturity of the virtual fitting system, there are a lot of problems, such as the expression of clothing color is not clear or deviation. In view of this, this paper proposes a 3D clothing color display model based on deep learning to support human modeling-driven. Firstly, the macro-micro adversarial network (MMAN) based on deep learning is used to analyze the original image, and then, the results are preprocessed. Finally, the 3D model with the original image color is constructed by using UV mapping. The experimental results show that the accuracy of the MMAN algorithm reaches 0.972, the established three-dimensional model is emotional enough, the expression of the clothing color is clear, and the difference between the color difference and the original image is within 0.01, and the subjective evaluation of volunteers is more than 90 points. The above results show that it is effective to use deep learning to build a 3D model with the original picture clothing color, which has great guiding significance for the research of character model modeling and simulation.

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Human Model-Based Active Driving System in Vehicular Dynamic Simulation

Cited by 6 publications

References 34 publications

Key Technologies of Vehicle Active Safety System Based on Computer Vision

Key Technologies of Vehicle Active Safety System Based on Computer Vision

Is Human Brain Activity During Driving Operations Modulated by the Viscoelastic Characteristics of a Steering Wheel?: An fMRI Study

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

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