Understanding the intentions of other road users increases subjective certainty, improves one's own performance and contributes to enhanced traffic safety. The perception and processing of situational characteristics are pivotal to develop an appropriate situation model as the basis of anticipation in dynamic traffic situations. To gather further insights into the process of situation comprehension, different situational characteristics were tested in a laboratory experiment using urban traffic scenarios. Participants had to indicate, in a two-step reaction method, the initial supposition and certain anticipation of another car merging into their own lane while being cognitively distracted (high versus low). Retrieval cues that enable the formation of causal relations and the development of a coherent mental representation increased the probability of anticipation. Furthermore, different situational characteristics were identified based on distinct underlying processing patterns. Cues that imply a close relationship with the target's behaviour (target cues) are assumed to require fewer inferences. They were less affected by increased cognitive load compared to other situational characteristics (context cues). These findings enhance the understanding of human anticipation and are discussed regarding the methodological approach and the generalisation of the proposed situational cues.
Look ahead: understanding cognitive anticipatory processes based on situational characteristics in dynamic traffic situations
IET Intelligent Transport Systems ; 14 , 4 ; 233-240
2020-03-06
8 pages
Article (Journal)
Electronic Resource
English
situation comprehension , different situational characteristics , increased cognitive load , situational cues , road safety , dynamic traffic situations , urban traffic scenarios , appropriate situation model , distinct underlying processing patterns , certain anticipation , enhanced traffic safety , initial supposition , behavioural sciences computing , psychology , road traffic , automobiles , cognition , road accidents , driver information systems , understanding cognitive anticipatory processes
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