Abstract
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.
Highlights
The unmanned vehicle is the product of the in-depth integration of the automotive industry with new-generation information technologies such as artificial intelligence, Internet of Things, and high-performance computing
EVALUATION OF COMPLEXITY OF THE DYNAMIC TRAFFIC ENVIRONMENT In this part, we evaluate the complexity of the dynamic traffic environment by quantitative model of dynamic environment complexity based on the improved gravitation model proposed in the paper, the model based on time to collision [24], and the model based on the gravitation model [25]
The main working in the paper is summarised as the following points: 1) According to the structural system of human-vehicleroad, we analyse the structure system of the complex traffic environment and relative static and dynamic characteristics of urban roads in the round
Summary
The unmanned vehicle is the product of the in-depth integration of the automotive industry with new-generation information technologies such as artificial intelligence, Internet of Things, and high-performance computing. Can be used to establish a quantitative model of complexity of the static traffic environment for unmanned vehicles in urban roads to evaluate the static traffic environment. 3) MODEL CONSTRUCTION We construct the quantitative model of the complexity of the static traffic environment for unmanned vehicles by the grey relation analysis method. The influencing factors of static environment complexity contain road grades, road surfaces, pavement structures, road alignment, road types, traffic facilities, illuminance, weather conditions, topographic features, surrounding scenes, and electromagnetic signals. Those influencing factors compose the comparison sequence, and attribute values of each influencing factor are assigned by using expert scoring. After completing the steps above, we can achieve the quantitative evaluation of complexity of the static traffic environment
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