TY - JOUR
T1 - Research on the Quantitative Evaluation of the Traffic Environment Complexity for Unmanned Vehicles in Urban Roads
AU - Yang, Shijuan
AU - Gao, Li
AU - Zhao, Yanan
AU - Li, Xurong
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Traffic environment complexity
KW - dynamic traffic environment
KW - quantitative evaluation model
KW - static traffic environment
KW - unmanned vehicles
UR - http://www.scopus.com/inward/record.url?scp=85100463060&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3054882
DO - 10.1109/ACCESS.2021.3054882
M3 - Article
AN - SCOPUS:85100463060
SN - 2169-3536
VL - 9
SP - 23139
EP - 23152
JO - IEEE Access
JF - IEEE Access
M1 - 9336653
ER -