A data–information–knowledge cycle for modeling driving behavior.

• An extensive literature review was conducted with keywords of "data collection", "information extraction", and "autonomous vehicles". • Over 160 studies has been screened and 27 studies have been selected for detailed analysis. • Selected studies focused on driving behavior modeling, with aspects of data collection and information extraction. • Findings highlighted the methods and equipment used for data collection, features collected, size and frequency of the data, and associated main challenges. • A data-analytics framework was developed with following components: data collection, data processing, data storage, data mining and analytics, data sharing, along with external legal and ethical considerations. • A data fusion framework was proposed with two layers of analytics, and a pathway to transfer the knowledge across different transport sectors. When talking about automation, "autonomous vehicles", often abbreviated as AVs, come to mind. In transitioning from the "driver" mode to the different automation levels, there is an inevitable need for modeling driving behavior. This often happens through data collection from experiments and studies, but also information extraction, a key step in behavioral modeling. Particularly, naturalistic driving studies and field operational trials are used to collect meaningful data on drivers' interactions in real–world conditions. On the other hand, information extraction methods allow to predict or mimic driving behavior, by using a set of statistical learning methods. In simple words, the way to understand drivers' needs and wants in the era of automation can be represented in a data–information cycle, starting from data collection, and ending with information extraction. To develop this cycle, this research reviews studies with keywords "data collection", "information extraction", "AVs", while keeping the focus on driving behavior. The resulting review led to a screening of about 161 papers, out of which about 30 were selected for a detailed analysis. The analysis included an investigation of the methods and equipment used for data collection, the features collected, the size and frequency of the data along with the main problems associated with the different sensory equipment; the studies also looked at the models used to extract information, including various statistical techniques used in AV studies. This paved the way to the development of a framework for data analytics and fusion, allowing the use of highly heterogeneous data to reach the defined objectives; for this paper, the example of impacts of AVs on a network level and AV acceptance is given. The authors suggest that such a framework could be extended and transferred across the various transportation sectors. [ABSTRACT FROM AUTHOR]