As a trend of industrial development, autonomous driving has been renewed as a hot research topic with the rapid increase of new technologies. Every year, many researchers devote themselves to the learning and research on autonomous driving technologies. However, due to the high barriers to entry in this interdisciplinary area, beginners often feel struggling even frustrating in their early learning process. Searching on the Web has become the most important way for people to gain knowledge; studies of user habits reveal that researchers engage in many online academic searching tasks involving asynchronous collaboration with others (e.g. collect relevant literature) to advance their researches. However, current web search engines are generally designed for a single user, searching alone, which are not friendly for researchers to collaborate with each other. To address this issue, we propose LogCanvas, a graph-based user history interface for search engines, to support researchers to conduct asynchronous collaborative web search (i.e., users are in a distinct remote location, with their own computer, carry out different search processes and save efforts by consuming previous users' search results). We take researchers in autonomous driving as an example to describe the development and usage of LogCanvas. In order to investigate the efficacy of LogCanvas, we extend the user scope of LogCanvas to general users and conducted an online crowd-powered experiment inviting 387 participants to use this platform. We studied users' behaviors and collected their feedback about user experience. The results indicate that LogCanvas could benefit users' asynchronous collaborative web search and their learning.

作为工业发展的趋势，随着新技术的迅速发展，自动驾驶已被更新为一个热门研究课题。每年，许多研究人员致力于自动驾驶技术的学习和研究。但是，由于跨学科领域的高准入门槛，初学者在早期学习过程中常常会感到挣扎甚至沮丧。在网络上搜索已成为人们获取知识的最重要方式。用户习惯研究表明，研究人员从事许多在线学术搜索任务，这些任务涉及与他人的异步协作（例如，收集相关文献）以推进他们的研究。但是，当前的网络搜索引擎通常是为单个用户设计的，只能单独搜索，这对研究人员之间进行协作并不友好。为了解决此问题，我们建议使用LogCanvas（一种基于图形的搜索引擎用户历史记录界面），以支持研究人员进行异步协作式网络搜索（即，用户位于不同的远程位置，并拥有自己的计算机，执行不同的搜索过程并通过使用以前的用户的搜索结果来节省工作量）。我们以自动驾驶研究人员为例，描述LogCanvas的开发和使用。为了调查LogCanvas的功效，我们将LogCanvas的用户范围扩展到普通用户，并进行了在线人群驱动的实验，邀请387名参与者使用此平台。我们研究了用户的行为，并收集了有关用户体验的反馈。