Decision-Making of Drivers Following Autonomous Vehicles: Developing a Bayesian Network on the Basis of Field Tests and Questionnaire Data

Abstract

With the development of autonomous driving technology, traffic mixed with human-driven vehicles (HDVs) and autonomous vehicles (AVs) has dominated transportation systems for a long period of time. Drivers' car-following decision-making in mixed traffic needs to be considered for traffic simulation and management policy formulation. This study aims to explore the differences in drivers' decision-making mechanisms when following AVs and HDVs. Data from a questionnaire survey and a field test are collected and employed to establish a Bayesian network for car-following decision-making process analysis and inference. The influences of driving habits and recognition of AVs on car-following decisions and the correlations among the four decision variables are analyzed. The four decision variables consist of the vehicle gap and acceleration in both the acceleration and deceleration phases. The results show that there are direct correlations among the four internal decision variables. Among the external variables, overspeeding and honking have distinct impacts on decisions made while following an AV. Moreover, regardless of whether they are in an acceleration or deceleration phase, most drivers tend to make gentler decisions when following AVs than when following HDVs. On the basis of the results, we propose some strategies for the traffic management of mixed traffic that are beneficial to traffic efficiency: (1) Improving drivers' recognition of AVs; (2) embedding the external sensing devices of AVs internally to make them visually similar to HDVs; and (3) establishing dedicated lanes for AVs. The research results have important reference significance for simulating car-following behavior, designing traffic control facilities and formulating policies under mixed traffic scenarios.