Evaluation Model of Intelligent Vehicle Test Conditions Based on Risk Degree and Complexity Degree

Studies on intelligent vehicles, among which the controlling method of intelligent vehicles is a key technique, have drawn the attention of industry and the academe. This study focuses on designing an intelligent lateral control algorithm for vehicles at various speeds, formulating a strategy, introducing the Gauss cloud model and the cloud reasoning algorithm, and proposing a cloud control algorithm for calculating intelligent vehicle lateral offsets. A real vehicle test is applied to explain the implementation of the algorithm. Empirical results show that if the Gauss cloud model and the cloud reasoning algorithm are applied to calculate the lateral control offset and the vehicles drive at different speeds within a direction control area of ±7°, a stable control effect is achieved.

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Evaluation of Waterway Navigation Environment Based on SPA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.97-98.960 ◽

2011 ◽

Vol 97-98 ◽

pp. 960-963

Author(s):

Hou Zhong Chen ◽

Yan Fei Tian

Keyword(s):

Basic Principle ◽

Evaluation Model ◽

Set Pair Analysis ◽

Safety Level ◽

Connection Degree ◽

Risk Degree ◽

Pair Analysis ◽

Navigation Safety

In order to evaluate waterway navigation environment more systemically, the Set Pair Analysis Evaluation Model for the environment was established according to basic principle of SPA, on basis of safety affection factors being analyzed. In accordance with risk degree of each factor, the expression of Connection Degree can be obtained, proceeding with estimating the safety of a channel. This model used, the waterway navigation safety level can be evaluated more objectively, comprehensively and systemically, and criticalities of affect or indeterminate factors gained in a quantitative way. Targeting measures could be implemented to improve navigation conditions to ensure navigation safety with the assessment conclusion being based on.

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State Estimation for 4WD In-Wheel Motor Electric Vehicle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.147 ◽

2014 ◽

Vol 610 ◽

pp. 147-155 ◽

Cited By ~ 2

Author(s):

Bo Lin Gao ◽

Hui Chen ◽

Shu Gang Xie ◽

Jin Feng Gong

Keyword(s):

State Estimation ◽

Electric Vehicle ◽

Kalman Filters ◽

Longitudinal Velocity ◽

The Other ◽

Test Results ◽

Sideslip Angle ◽

Extended Kalman Filters ◽

Test Conditions ◽

Vehicle Test

This article proposed a Double Extended Kalman Filters (Double EKFs) observer to estimate states of 4WD in-wheel motor electric vehicle. One EKF observer was used to estimate vehicle longitudinal velocity. The other EKF observer was employed to estimate vehicle sideslip angle. The structure has two benefits: Firstly, two EKFs reduce the order of the wholly state observer, so that calculation load could be lower. Secondly, the two EKFs could be adjusted easier and more independently. The paper investigates the feasibility of this observer in two vehicle test conditions. The test results indicate the effectiveness of the Double EKFs State estimation.

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Risk Assessment on Ground Fissure Disasters of Xi’an Metro Line 3

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.255-260.3841 ◽

2011 ◽

Vol 255-260 ◽

pp. 3841-3845

Author(s):

Zhen Wei Jiang ◽

Qi Yao Wang

Keyword(s):

Risk Assessment ◽

Numerical Analysis ◽

Model Test ◽

Evaluation Model ◽

Geological Environment ◽

Ground Fissure ◽

Ground Fissures ◽

Risk Degree ◽

Sedimentation Value

Due to special geological environment in Xi’an, serious ground fissure disasters will occur to the Metro construction. According to the planning, Metro Line 3 will meet with the ground fissures at 15 segments. Based on the results of model test and numerical analysis, the minimum sedimentation value of ground fissure arousing disasters on metro was determined as . By using the evaluation model of risk degree as R = P×V, risk degrees of each segment where the metro crossed ground fissures was calculated. The results showed that in normal design circumstances, there are eight ultra-high risks segments and two high risks segments. Therefore, special measures should be taken to reduce risks for these segments with high risks or above.

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Vehicle Control Based on Fuzzy Evaluation Knowledge Obtained by Coefficients of the ARMA Model

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.1997.p0008 ◽

1997 ◽

Vol 1 (1) ◽

pp. 8-9

Author(s):

Hiroshi Takahashi ◽

Keyword(s):

Fuzzy Logic ◽

Evaluation Model ◽

Subjective Evaluation ◽

Arma Model ◽

Vehicle Control ◽

Intelligent Vehicle ◽

Fuzzy Evaluation ◽

Feedback Control System ◽

Driving Task ◽

Evaluation Feedback

This paper presents a study on an intelligent vehicle that uses the driver's subjective evaluation model. The subject's driving task is to follow the car in front of him (the first car) and to keep the headway distance between the two cars consistent. Using ARMA model and fuzzy logic I created a subjective evaluation model that can predict the driver's subjective evaluation while performing this driving task. I realized that a subjective evaluation feedback control system would be very useful in future vehicles.

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Design and Implementation of a Miniature Intelligent Vehicle Test Platform

International Journal of Smart Home ◽

10.14257/ijsh.2013.7.5.35 ◽

2013 ◽

Vol 7 (5) ◽

pp. 367-376

Author(s):

Ning Li ◽

Guangming Xiong ◽

Weilong Song ◽

Jianwei Gong ◽

Huiyan Chen

Keyword(s):

Intelligent Vehicle ◽

Design And Implementation ◽

Test Platform ◽

Vehicle Test

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Exploring the Relationships between Subjective Evaluations and Objective Metrics of Vehicle Dynamic Performance

Journal of Advanced Transportation ◽

10.1155/2018/2638474 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Jianyou Zhao ◽

Jing Liu ◽

Liping Yang ◽

Ping He

Keyword(s):

Probabilistic Neural Network ◽

Evaluation Model ◽

Subjective Evaluation ◽

Dynamic Performance ◽

Correlation Coefficients ◽

Vehicle Dynamic ◽

Subjective Evaluations ◽

Objective Metrics ◽

Vehicle Test ◽

The Individual

This study explored the relationships between subjective evaluations and objective metrics of vehicle dynamic performance. First, a real vehicle test was performed to measure the acceleration performance under different conditions, and participants’ subjective evaluations of the acceleration performance were investigated. Second, correlation analysis was conducted to explore relationships between each subjective evaluation and its corresponding objective metric as well as between the overall subjective evaluation and three individual subjective evaluations. Finally, an overall subjective evaluation model related to the three objective metrics was established based on the Probabilistic Neural Network (PNN). The analysis results demonstrated that the correlation coefficients of the three groups of data were greater than 0.5 and that each subjective evaluation was significantly correlated with its corresponding objective metric. The individual subjective evaluation of the climbing acceleration performance had the largest effect on the overall subjective evaluation, with a correlation coefficient of 0.47. The established overall subjective evaluation model was relatively reliable, with a prediction accuracy of 90%. This study furthered the existing knowledge of the methods for evaluating vehicle dynamic performance. The proposed overall subjective evaluation model improves the reliability of vehicle dynamic evaluations and offers a theoretical basis for vehicle manufacturers to improve automobile performance.

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