Reducing uncertainty by anticipation in traffic – The effect of situational characteristics and criticality on behavioral, subjective, and psychophysiological parameters

Abstract

Uncertainty in traffic may have fatal consequences when operating a vehicle. Enabling drivers to anticipate the behavior of other traffic participants can help reduce uncertainty and thus increase traffic safety. A multi-method approach using behavioral, subjective, and psychophysiological measures was applied to investigate the relation of uncertainty and anticipation. Participants (N = 30) observed simulated, video-based urban traffic scenarios while skin conductance responses were recorded. They had to indicate, by pressing a button, (1) the moment they first thought another vehicle might merge into their lane (low certainty anticipation) and (2) the moment they were sure another vehicle would merge (high certainty anticipation). Situational characteristics served as anticipatory cues that helped predict the other vehicle’s action. On the one hand, in this study, target cues (which are clearly related to the target’s activity) served to indicate an imminent lane change. On the other hand, context cues represented visible precursors in the traffic environment, e.g. a traffic sign pointing to upcoming road work. In addition, causal cues were used to determine a reason for the other vehicle to change lanes (in this instance, a construction site blocking the lane). These situational characteristics, as well as the situational criticality, were manipulated to gain insights into factors influencing the process of anticipation. Results offered an effect of target cue moderated by criticality: especially in more critical situations, the anticipation rate and subjective certainty increased, and physiological activation was reduced with target cues. Overall, the anticipatory performance was found to be a predictor of subjective certainty (through its impact on skin conductance responses). The findings are discussed in the context of the methodological approach for applications in traffic.