Road-Feature-Based Multiparameter Road Complexity Calculation Model of Off-Road Environment

Zhao, Yanan; Li, Zhiwei; Gao, Li; Xiong, Jian

doi:10.1155/2018/1952792

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…12, the instantaneous dynamic complexity of the unmanned vehicle p and the variation amplitude decrease gradually with the speed of the In Fig. 13, the instantaneous dynamic complexity of the unmanned vehicle p decreases firstly and then increases with time t. This kind of change trend results from the difference in the increasing amplitude of the numerator and the denominator in (16). When the unmanned vehicle p accelerates at a constant acceleration, both speed of the unmanned vehicle p and distance between vehicles increase.…”

Section: ) Comparation Analysismentioning

confidence: 96%

“…Some researchers have conducted quantitative research on the traffic environment complexity. According to the analysis method of road roughness and the principle of Analytic Hierarchy Process, Zhao et al [16], [17] proposed a new road-feature-based multi-parameter road complexity calculation model of the off-road environment which regarded three-dimensional scale, average slope, and adhesion characteristics of travelable area as road indicators. Based on the analysis of the combat environment in the new version of the US Army's "Outline of Operations", Li et al [18] defined the typical working environment of unmanned vehicles in rural areas.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Quantitative Evaluation of the Traffic Environment Complexity for Unmanned Vehicles in Urban Roads

et al. 2021

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The primary goal of the paper is to explore the human-vehicle-road interaction mechanism in the traffic environment and evaluate the traffic environment complexity for unmanned vehicles in urban roads. In particular, we propose the quantitative evaluation models of the traffic environment complexity for unmanned vehicles in urban roads in the paper. Specifically, the structure system of the complex traffic environment in urban roads is dissected from the aspect of human-vehicle-road, laying the basis for proposing influencing factors of traffic environment complexity. We divide the complex traffic environment into the static traffic environment and the dynamic traffic environment in light of relative static and dynamic characteristics of various environmental elements. For the complexity of the static traffic environment, the quantitative evaluation model is established by the grey relation analysis method that converts static environment complexity into the relation degree of static complexity's influencing factors. For the complexity of the dynamic traffic environment, the quantitative evaluation model is established based on the improved gravitation model that introduces the concepts of equivalent mass and the contribution degree of the unmanned vehicles' driving strategy. Besides, we evaluate the traffic environment complexity in the designed scenario by quantitative models proposed in the paper and existing evaluation models of traffic environment complexity in urban roads. The calculating process and results show that the proposed quantitative models of traffic environment complexity are more convenient and more reasonable, which provide a new idea and a method to evaluate the traffic environment complexity.

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Section: ) Comparation Analysismentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Research on the Quantitative Evaluation of the Traffic Environment Complexity for Unmanned Vehicles in Urban Roads

et al. 2021

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“…Interestingly, the driving force required to follow the lead truck is slightly lower by approximately 400N, which translates to improved fuel economy. However, as shown in Figure5(d), there is a significant initial error in the workshop distance due to the controller's conflict between tracking speed and workshop distance when solving real-time optimization problems [19]. Although the workshop distance error fluctuates in the early stages, it remains consistently within the safe range and meets the safety requirements for platooning driving.…”

Section: Design and Test Of Queue Simulation Conditionmentioning

confidence: 98%

Delay compensation integrated distributed model predictive platooning control for intelligent trucks

Zhao,

Luo,

Zeng

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…In summary, there are the following problems in the quantitative assessment of traffic environment complexity [ 18 , 19 , 20 ]. Firstly, the factors considered in the assessment of traffic environment complexity are not comprehensive, the models are relatively simple, and the impact of different environmental factors on driving behavior and complexity is not analyzed from multiple dimensions, such as physical attributes and state parameters [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Traffic Risk Environment Impact Analysis and Complexity Assessment of Autonomous Vehicles Based on the Potential Field Method

Cheng

Liu

Gao

et al. 2022

IJERPH

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Although autonomous vehicles have introduced a promising potential for improving traffic safety and efficiency, ensuring the safety of autonomous vehicles in complex road traffic environments is still a huge challenge to be tackled. To quickly quantify the potential risk factors of autonomous vehicles in traffic environments, this paper focuses mainly on the influence of the depth and breadth of the environment elements on the autonomous driving system, uses the potential field theory to establish a model of the impact of the environmental elements on the autonomous driving system, and combines AHP to quantify equivalent virtual electric quantity of each environment element, so as to realize the quantitative evaluation of the traffic environment complexity. The proposed method comprehensively considers the physical attributes and state parameters of the environmental elements, which compensates for the fact that the shortage of the factors considered in the traffic environment complexity assessment is not comprehensive. Finally, a series of experiments was carried out to verify the reliability of our proposed method. The results show that the complexity of the static elements is determined only by the physical attributes and shape of the obstacle; the complexity of the dynamic elements is determined by the movement of the obstacle and the movement of the autonomous vehicle, and the comprehensive complexity mainly depends on the complexity of their dynamic elements. Compared with other methods, the complexity evaluation values are generally consistent, the absolute percentage error of the majority of samples was within ±5%, and the degree of deviation was −1.143%, which provides theoretical support for autonomous vehicles on safety and the risk assessment in future.

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Road-Feature-Based Multiparameter Road Complexity Calculation Model of Off-Road Environment

Cited by 7 publications

References 23 publications

Research on the Quantitative Evaluation of the Traffic Environment Complexity for Unmanned Vehicles in Urban Roads

Research on the Quantitative Evaluation of the Traffic Environment Complexity for Unmanned Vehicles in Urban Roads

Delay compensation integrated distributed model predictive platooning control for intelligent trucks

Traffic Risk Environment Impact Analysis and Complexity Assessment of Autonomous Vehicles Based on the Potential Field Method

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