A study of mixed-traffic lane change decision for connected and autonomous vehicles considering driving styles

With the rapid development of driverless technology, it is inevitable that self-driving cars and artificial cars will share the right of way. How to make reasonable and efficient lane change decisions in mixed-flow environment is extremely important. At present, there are abundant studies on the lane-changing model of intelligent connected vehicles in mixed-flow environment, but there is a lack of in-depth research on the influence of vehicles in the target lane on lane-changing behavior, and the influence of different driving styles of potentially conflicting vehicles on lane-changing behavior is not considered. To address the lane-changing and merging behavior of intelligent connected autonomous vehicles (CAVs) when potential conflicting vehicles are human-driven, this study incorporates the influence of various human driving styles into the decision-making process. A multi-attribute lane-change decision model based on entropy weighting is developed, aiming to achieve more favorable conditions and higher lane-changing benefits. This approach offers a novel perspective and method for autonomous vehicle lane-change decision-making during interactions with human-driven vehicles.