Evaluation Model of Harmony with Traffic Based on Neural Network

Abstract:

In order to study the subjective and objective mapping evaluation model of harmony with traffic for autonomous vehicles， this paper takes the highway on-ramp as a research scenario. First， based on the interaction sample data of natural driving data， a mapping evaluation model is constructed with objective index data and subjective evaluation results as model inputs and outputs， and objective indexes include average driving speed， side velocity， TTC， relative side velocity， deceleration degree of critical traffic participant and so on. Then， the influence of data preprocessing method and neural network type on the evaluation effect is analyzed using the 2×2 cross-comparison test. The results show that the precision of the BP neural network model and the Dropout neural network model based on Min-Max scaling is 95.71% and 80.00% respectively， and the precision of the BP neural network model and the Dropout neural network model based on staircase-function normalization is 94.60% and 73.25% respectively. It can be seen that the evaluation effect of the model is good， and the objective index set can express the evaluation of experts on the harmony with traffic. In terms of modeling method， the performance of the mapping evaluation model based on BP neural network is better than that of the Dropout neural network model， which can get more accurate evaluation results. In terms of data preprocessing method， the Min-Max scaling method can eliminate the magnitude difference between the data and keep the difference of objective index data between different samples， which can improve the evaluation accuracy of the mapping evaluation model. Therefore， when the sample size is small， the evaluation effect of the mapping evaluation model based on Min-Max scaling and the BP neural network is better.