No abstract

No abstract

No abstract

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.

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.