Research on the quantitative evaluation system for unmanned ground vehicles

Xiong, Guangming; Zhao, Xijun; Liu, Haiou; Wu, S. M.; Gong, Jianya; Zhang, Haojie; Tan, Huachun; Chen, Huiyan

doi:10.1109/ivs.2010.5548144

Cited by 14 publications

(9 citation statements)

References 8 publications

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“…The EAHP method is used to determine the weight of factors. It overcomes the ambiguity problem of AHP [16][17][18] in solving expert's experience judgement problems, as it does not need to judge the consistency of judgement matrix, and effectively avoids a lot of trial work required in AHP. Chaos theory quantitative analysis for the driving track and grey relational analysis can better reflect the actual situation and can easily be transformed into a specific score.…”

Section: Introductionmentioning

confidence: 99%

Research on an Intelligent Behavior Evaluation System for Unmanned Ground Vehicles

Sun

Meng

2018

Energies

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A scientific and reasonable evaluation system for unmanned ground vehicles (UGVs) is very important. This paper studies the intelligent behavior of UGVs, and also proposes a comprehensive evaluation system for this intelligent behavior. The test and evaluation system includes the test content design, the test environment design, the test methods and the evaluation method. Using a hierarchical design approach, the test content is designed to be stage by stage, moving from simplicity to complexity and from individual modules to the entire vehicle. The hierarchical test environment is established according to the test content levels. The extension analytic hierarchy process (EAHP) has a better advantage than the analytic hierarchy process (AHP) to avoid the problem of the ambiguity of expert experience judgment and the consistency of the judgment matrix in determine the weight of each evaluation index. Using chaos theory to calculate the Lyapunov index, the quality of the trajectory of the UGV is characterized. The grey relational analysis method is used to analyze the correlation between the comparison series and the reference series, and a comprehensive quantitative result of the intelligent behavior of the UGV is obtained. The experiment shows that the intelligent behavior evaluation system of the UGV is scientific and effective.

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Section: Introductionmentioning

confidence: 99%

Research on an Intelligent Behavior Evaluation System for Unmanned Ground Vehicles

Sun

Meng

2018

Energies

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“…U-Turn evaluation environment has been accepted for more competitions in recent years, which was evaluated by analytic hierarchy process [4] . Quantitative evaluation of intelligent behavior of unmanned vehicles based on fuzzy analytic hierarchy process was proposed [5][6] . Recently, U-turn environment is regarded as an important test site since it can reflect technological level of unmanned vehicles.…”

Section: Introductionmentioning

confidence: 99%

Application of Gray Correlation and Improved AHP to Evaluation on Intelligent U-Turn Behavior of Unmanned Vehicles

Dong

Zhao

Gao

2015

2015 8th International Symposium on Computational Intelligence and Design (ISCID)

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Gray correlation analysis combined with improved AHP (Analytic Hierarchy Process) method is applied to evaluate intelligent U-turn behavior of unmanned vehicles. On the basis of establishing the evaluation index system of intelligent U-turn behavior, improved AHP is used to determine the weight of every evaluation index, and then the gray correlation comprehensive evaluation model is constructed, which can be directly converted to the score and easily compared or ordered by the results. The results of applying the scheme to the evaluation of "Future Challenge 2012" show that the scores of four racing vehicles are 94.6, 60.5, 80.9, 59.5 and the ranking is Vehicle 1>Vehicle 3>Vehicle 2>Vehicle 4, and this ranking obtained by the method is consistent with the result given by the referee group. The proposed method has a certain value, which can provide theoretical reference for evaluation of intelligent behaviors of unmanned vehicles.

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“…"Future Challenge 2009" was held in June 2009 [5]; the race used the third-party testing to evaluate the implementation efficiency of intelligent vehicles in competitions. This attempt has promoted and facilitated the innovation and development of vehicle verification platform [6]. "Future Challenge 2012" competition was held in November 2012, Chifeng City, Inner Mongolia [7].…”

Section: Introductionmentioning

confidence: 99%

The Entropy-Cost Function Evaluation Method for Unmanned Ground Vehicles

Zhao

Meng

Gao

2015

Mathematical Problems in Engineering

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To improve the technology of unmanned ground vehicles, it is necessary to conduct a proper evaluation on various technologies. Previous evaluation methods are mainly based on completion of the task; this may mislead most of teams of unmanned ground vehicles using a conservative strategy during the evaluation. In this paper, a new evaluation method is proposed. Based on typical working conditions including intersection, car-following, and obstacle-avoiding, the new evaluation indicator system is established, and the entropy-cost function method is applied to the comprehensive evaluation of unmanned ground vehicles. As reported in a numerical example, the proposed evaluation method can get a quantitative result that authentically reflects the intelligent behavior level of unmanned ground vehicles.

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Research on the quantitative evaluation system for unmanned ground vehicles

Cited by 14 publications

References 8 publications

Research on an Intelligent Behavior Evaluation System for Unmanned Ground Vehicles

Research on an Intelligent Behavior Evaluation System for Unmanned Ground Vehicles

Application of Gray Correlation and Improved AHP to Evaluation on Intelligent U-Turn Behavior of Unmanned Vehicles

The Entropy-Cost Function Evaluation Method for Unmanned Ground Vehicles

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