Abstract
Lane change manoeuvres pose high demands on the driver. Driver intent information is supposed to provide lane change assistance specifically when required, thus increasing acceptance and traffic safety. Based on an on-road study including 60 participants, the I-FAS investigated lane change behaviour at different levels of analysis. The present chapter shows the analysis of lane change predictors on the behavioural, strategic, manoeuvring and control level. Considering driver characteristics on the strategic/behaviour level, familiarity with the route resulted as the most important predictor for the number of lane changes performed per trip. Analyses at the manoeuvring level showed that lane change manoeuvres need to be further subdivided into subtypes with different requirements for prediction. Driver behaviour – especially automated glance behaviour at the control level – differed considerably between e.g. lane changes due to a slower vehicle ahead and lane changes on an added lane. Mirror glance patterns for specific lane change types resulted as promising and quite stable intention predictors, even before the activation of the turn signal. However, the interpretation of glances as indicator for lane change intention is vague without the integration of information about the driving situation. Therefore, a realtime lane change prediction algorithm was developed integrating driver behaviour, vehicle parameters as well as data from the vehicles’ surroundings in a Bayesian Network.
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Beggiato, M. et al. (2018). Lane Change Prediction: From Driver Characteristics, Manoeuvre Types and Glance Behaviour to a Real-Time Prediction Algorithm. In: Bengler, K., Drüke, J., Hoffmann, S., Manstetten, D., Neukum, A. (eds) UR:BAN Human Factors in Traffic. ATZ/MTZ-Fachbuch. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-15418-9_11
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