Human Factors of Automated Driving: Predicting the Effects of Authority Transitions on Traffic Flow Efficiency

Human Factors of Automated Driving: Predicting the Effects of Authority Transitions on Traffic Flow Efficiency

Varotto, S.F.
Hoogendoorn, R.G.
Van Arem, B.
Hoogendoorn, S.P.

Civil Engineering and Geosciences

Transport & Planning

2014-11-13

Automated driving potentially has a significant impact on traffic flow efficiency. Automated vehicles, which possess cooperative capabilities, are expected to reduce congestion levels for instance by increasing road capacity, by anticipating traffic conditions further downstream and also by accelerating the clearance of congestion. However, the effects of automation on traffic flow efficiency may be considerably influenced by human factors such as user acceptance and behavioural adaptations of drivers. Under certain traffic situations, drivers could prefer to disengage the automated system and transfer to a lower level of automation or are forced to switch off by the system (e.g. in case of sensor failure). These transitions between different levels of automation are called authority transitions and can significantly affect the longitudinal and lateral dynamics of vehicles. Microscopic simulation software packages can be used to ex ante evaluate the impact of automated vehicles on traffic flow efficiency. Currently, mathematical models describing car-following and lane changing behaviour are not able to adequately describe and predict authority transitions. In order to develop an adequate model of driving behaviour for automated vehicles including these authority transitions, an empirically underpinned theoretical framework is needed where human factors are accounted for. In the proposed research, we aim at developing this theoretical framework, which serves as the basis for the prediction of effects of automated driving on traffic flow efficiency. In order to determine the real-life effect of automation on traffic flow efficiency, firstly, empirical data from Field Operational Test and driving simulation experiments will be collected and analysed. Secondly, microscopic traffic flows models incorporating human factors will be developed: within this framework, authority transitions will be investigated taking into account intra- and inter-driver heterogeneity. Thirdly, the effects of different penetration rates of automated vehicles and different levels of automation on traffic flow efficiency will be investigated.

automation
authority transitions
human factors
microscopic modelling
traffic flow efficiency

http://resolver.tudelft.nl/uuid:1ef79a77-c274-4ec0-a3e3-32a2d84f892f

TRAIL

Proceedings of the 2nd TRAIL Internal PhD Conference, Delft, The Netherlands, 13 November 2014

Institutional Repository

conference paper

(c) 2014 The Author(s)