Benefit from the progress in the field of explainable artificial intelligence (XAI), explanations have been increasingly prospective in the autonomous vehicle (AV) context. Providing explanations has been proved to be vital for human-AV interaction, but what and how to explain are still to be addressed. This study seeks to bridge the areas of XAI and human-AV interaction by combining perspectives of both users and researchers. In this paper, a conceptual framework of explanation models was proposed to indicate what aspects to explain in human-AV interaction. Based on the framework, we introduced a scenario-based and question-driven method, i.e., the SQX-canvas, to guide the workflow of generating explanations from users’ demands in a certain AV scenario. To make an initial validation of the method, a co-design workshop involving researchers and users was conducted with four AV scenarios provided in forms of video clips. Participants produced explanation concepts and expressed their attitudes towards the AV scenarios following the “scenario, question and explanation” process. It was apparent that users’ demands of explanations varied across scenarios, and findings as well as limitations were discussed. This method could provide implications for research and practice on facilitating transparent human-AV interaction.