Integration Degree Modeling and Analysis for Autonomous Vehicles in Road Testing

Abstract:

An evaluation framework based on five evaluation indicators was proposed. The Pearl growth curve function and negative exponential function were selected to standardize the positive and negative indicators， respectively. Then， the expert scoring method and analytic hierarchy process were used to determine the weight of each indicator. Finally， the integration degree model of autonomous driving road testing was built. The case study， based on the measured data and simulation results of automatic driving road testing under two types of urban road scenarios and one type of highway scenario in Shanghai， is conducted to verify the validity and effectiveness of the integration degree model. The results show that the risk-avoiding disengagement frequency is the most critical evaluation indicator which characterizes the integration degree of autonomous vehicles into existing road traffic system under both urban road scenarios and highway scenario； the integration degree under the highway scenario is significantly higher than that under the urban road scenario； increasing the testing mileage， the testing duration and the complexity of testing scenarios can promote the maturity of autonomous vehicle technology.