Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews were conducted in April and May 2015 with twelve expert researchers in the field of Human Factors (HF) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AV's capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers, and vulnerable road users. Additionally, driver training programs may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees' statements-which appear to be in line with a long history of work on human factors research, and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.

No abstract

No abstract

No abstract

No abstract

No abstract

We present a new method and tool for activity modelling through qualitative sequential data analysis. In particular, we address the question of constructing a symbolic abstract representation of an activity from an activity trace. We use knowledge engineering techniques to help the analyst build an ontology of the activity, that is, a set of symbols and hierarchical semantics that supports the construction of activity models. The ontology construction is pragmatic, evolutionist and driven by the analyst in accordance with their modelling goals and their research questions. Our tool helps the analyst define transformation rules to process the raw trace into abstract traces based on the ontology. The analyst visualizes the abstract traces and iteratively tests the ontology, the transformation rules and the visualization format to confirm the models of activity. With this tool and this method, we found innovative ways to represent a car-driving activity at different levels of abstraction from activity traces collected from an instrumented vehicle. As examples, we report two new strategies of lane changing on motorways that we have found and modelled with this approach

This paper presents the driver model developed by INRETS in the ISi-PADAS project, with the aim to dynamically simulate driver's mental activities carried out while driving. The methodology supporting this model development is based on empirical data collection on driving simulator, in the frame of a carfollowing task. After presenting the theoretical foundations of the modelling approach and the empirical data analysis, the functional architecture of our COgnitive Simulation MOdel of the DRIVEr (COSMODRIVE) will be described, and the type of results liable to be obtained through simulations on a virtual Vehicle-Environment platform (SiVIC) will be presented. Then, the conclusion will briefly examine the perspectives of the model applications for driving aids virtual design.