Accident Analysis and Prevention 42 (2010) 2158–2164 Contents lists available at ScienceDirect Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap Expectations while car following—The consequences for driving behaviour in a simulated driving task Elke Muhrer ∗ , Mark Vollrath Technische Universität Braunschweig, Department of Engineering and Traffic Psychology, Gaußstrasse 23, 38106 Braunschweig, Germany article info Article history: Received 25 May 2010 Received in revised form 12 July 2010 Accepted 15 July 2010 Keywords: Accident analysis Car following Driver modelling Expectation Rear-end crash abstract The purpose of this study was to better understand the causes of driver errors in the context of rear-end crashes. When drivers move in traffic they generate an overall assessment of the driving situation and what will happen in the near future. Certain cues in the traffic environment may create an expectation that some specific action is required. The more relevant cues are present, the more the driver will expect that some kind of intervention may be required. In contrary, if hardly any or no relevant cues are present, the driver does not anticipate that an imminent reaction will be necessary. This idea is supported by results from accident analyses which showed that in many cases, rear-end crashes occur in situations which are usually easy to handle (e.g. straight roads, low traffic density). In these situations, drivers may not anticipate that the driver in front will brake and they are thus following too closely to be able to react in time when the front vehicle suddenly brakes or stops. In order to test this hypothesis experimentally, in a driving simulator experiment different expectations were generated by varying the behaviour of a lead car (different braking behaviour, signalling or not before a turn). Driver behaviour was examined after these variations. The analyses partially confirm the influence of different expectations generated by the lead car’s behaviour in the first phase of the scenario. Drivers with a respective expectation reacted faster when the car in front suddenly braked and signalled their manoeuvre before turning right at an intersection. However, during a car following phase, drivers did not adapt their speed or distance depending on this expectation. These results can be used to adapt a driver assistance system in car following situations. This should warn and intervene, especially in cases when drivers do not foresee the need for action and therefore cannot react in time. © 2010 Elsevier Ltd. All rights reserved. 1. Introduction The EU-Project ISi-PADAS (Integrated Human Modelling and Simulation to Support Human Error Risk Analysis of Partially Autonomous Driver Assistance Systems) aims to predict and sim- ulate driving behaviour with digital human models. Knowledge about how drivers interact with assistance systems and the iden- tification of possible misuse facilitates system improvements in early stages of the development process. Furthermore, this inte- gration of human driving behaviour helps develop systems, which are adapted to drivers’ requirements and is expected to increase the reliability of the assistance system and also traffic safety. Accident analyses indicate that in many accidents there is no time for a warning to trigger a reaction of the driver (Vollrath et al., 2006; Muhrer and Vollrath, 2009) and assistance systems ought to intervene autonomously to avoid the accident. This autonomous intervention should only occur when there is no other solution to ∗ Corresponding author. Tel.: +49 531 391 3648; fax: +49 531 391 8181. E-mail address: e.muhrer@tu-bs.de (E. Muhrer). avoid the crash and should not happen when the driver can con- trol the situation. Therefore it is required to know that the driver is not able to react and the situation cannot be handled safely by him. Driver models may help to predict the condition of the driver (Casucci et al., 2010; Hollnagel et al., 2003; Salvucci, 2006,). In this project, this modelling approach is tested in a car following sce- nario, because rear-end crashes are a common type of accident (Baldock et al., 2005; Najm et al., 2003) and state-of-the-art sen- sors and actuators now available are capable of preventing such crashes. To develop a driver model for this type of accident, it is necessary to understand what kinds of driver errors provoke the accident itself. Accident analyses are used to generate hypothe- ses about the causes of driver errors. These hypotheses are then tested in empirical investigations in a driving simulator to create the foundation for the driver modelling. The importance of examining rear-end collisions is demon- strated by numerous accident statistics. Watanabe and Ito (2007) showed that in Japan, rear-end collisions were the most common type of accident (35%) in 2005. Their report was based on the ITARDA (Institute For Traffic Accident Research and Data Analysis) database. In Germany, the German Federal Statistical Office docu- 0001-4575/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2010.07.009