HFauto

Silvia Francesca Varotto

Early Stage Researcher 13, Delft University of Technology

Silvia joined the Transport and Planning Department of Delft University of Technology in January 2014 as a Marie Curie Fellow (Early Stage Researcher) in the HF Auto ITN project. She is currently investigating the effects of automated driving on traffic flow efficiency using microscopic traffic flow simulations. Her research focuses on the role of authority transitions between different levels of automation and manual driving.

Silvia carried out her master thesis at Ecole Polytechnique Fédérale de Lausanne (Switzerland) developing Hybrid Choice Models in transport mode choices. She holds a Laurea Magistrale in Civil Engineering specialization in Transport (2013) and a Laurea Triennale in Civil and Environmental Engineering (2011) from Università degli studi di Trieste (Italy).

Interests

  • Driving Behaviour and Behavioural Adaptations
  • Automated Driving and Intelligent Vehicle Systems
  • Traffic Flow Modelling
  • Human Factors
  • Transport Safety

Project interests: Predicting the Effects of Transient Manoeuvres on Traffic Flow Efficiency.

Automated driving potentially has a significant impact on traffic flow efficiency. Automated vehicles which are able to show cooperative behaviour are expected to reduce congestion levels by increasing road capacity, by anticipating traffic conditions further downstream and also by accelerating the clearance of congestion. Indeed, automated vehicles can result in higher outflows from congestion by increasing accelerations and reducing reaction times to zero.

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. These transitions between different levels of automation are defined as authority transitions. Authority transitions could significantly affect the longitudinal and lateral dynamics of vehicles and may consequently influence traffic flow efficiency considerably.

Using empirical data, mathematical models of driving behaviour of manually driven and automated vehicles can be estimated. These models can be implemented in microscopic simulation software packages to ex ante evaluate the impact of automated vehicles on traffic flow efficiency with varying penetration rates. Currently, mathematical models describing car-following and lane changing behaviour do not account for authority transitions.

Direction of research

Objective 1: Empirics of Automated Driving.

To investigate the role of human behaviour (i.e. longitudinal and lateral control) in vehicles of different levels of automation (i.e. driver assistance, partial automation, high automation), exploring variations within drivers and differences between drivers. The impact of human behaviour in transient manoeuvres (e.g. merging, splitting, platoon entry, platoon exit and authority transitions between different levels of automation) will be explored exhaustively. For this purpose, empirical data from field operational tests and driving simulation experiments will be analysed.

Objective 2: Theoretical Framework for Human Factors of Automated driving.

In order to develop an adequate model of driving behaviour for automated vehicles including authority transitions, an empirically underpinned theoretical framework is needed where human factors are accounted for.

Objective 3: Modelling of Automated Driving in case of Transient Manoeuvres.

To develop mathematical models of driving behaviour that incorporate transient manoeuvres in relation to automated vehicles. The aim of the inclusion of transient manoeuvres is to explore the potential effects on traffic flow efficiency determined by the presence of automated driving vehicles in mixed traffic conditions (i.e. simultaneous presence of vehicles with different levels of automation and different penetration levels).

Objective 4: Effects of Automated Driving on traffic flow efficiency.

To evaluate the effect of automated driving on traffic flow efficiency in mixed traffic conditions using the microscopic traffic flows models developed.

 

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