The Acceptance of Independent Autonomous Vehicles and Cooperative Vehicle-Highway Autonomous Vehicles

Abstract

The public’s acceptance of independent autonomous vehicles and cooperative vehicle-highway autonomous vehicles is studied by combining the structural equation model and an artificial neural network. The structural equation model’s output variables are used as the input variables of the artificial neural network, which compensates for the linear problem of the structural equation model and ensures the accuracy of the input variables of the artificial neural network. In order to summarize the influencing factors of autonomous vehicles acceptance, the Unified Theory of Acceptance and Use of Technology model was expanded by adding two variables: risk expectation and consumer innovation. The results show that social influence is the strongest predictor of the acceptance of independent autonomous vehicles. The most significant factor of the cooperative vehicle-highway autonomous vehicles’ acceptance is effort expectation. Additionally, risks, performance, existing traffic conditions, and personal innovation awareness also significantly affect autonomous driving acceptance. The research results can provide a theoretical basis for technology developers and industry managers to develop autonomous driving technology and policymaking.

Keywords

• acceptance
• artificial neural network
• autonomous technology
• structural equation model
• unified theory of acceptance and use of technology model