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  • Title: Modelling discomfort: How do drivers feel when cyclists cross their path?
    Author: Boda CN, Dozza M, Puente Guillen P, Thalya P, Jaber L, Lubbe N.
    Journal: Accid Anal Prev; 2020 Oct; 146():105550. PubMed ID: 32947207.
    Abstract:
    Many cyclist fatalities occur on roads when crossing a vehicle path. Active safety systems address these interactions. However, the driver behaviour models that these systems use may not be optimal in terms of driver acceptance. Incorporating explicit estimates of driver discomfort might improve acceptance. This study quantified the degree of discomfort experienced by drivers when cyclists crossed their travel path. Participants were instructed to drive through an intersection in a fixed-base simulator or on a test track, following the same experimental protocol. During the experiments, three variables were controlled: 1) the car speed (30, 50 km/h), 2) the bicycle speed (10, 20 km/h), and 3) the bicycle-car encroachment sequence (bicycle clears the intersection first, potential 50 %-overlap crash, and car clears the intersection first). For each trial, a covariate, the car's time-to-arrival at the intersection when the bicycle appears (TTAvis), was calculated. After each trial, the participants were asked to report their experienced discomfort on a 7-point Likert scale ranging from no discomfort (1) to maximum discomfort (7). The effect of the three controlled variables and the effect of TTAvis on drivers' discomfort were estimated using cumulative link mixed models (CLMM). Across both experimental environments, the controlled variables were shown to significantly influence discomfort. TTAvis was shown to have a significant effect on discomfort as well; the closer to zero TTAvis was (i.e., the more critical the situation), the more likely the driver reported great discomfort. The prediction accuracies of the CLMM with all three controlled variables and the CLMM with TTAvis were similar, with an average accuracy between 40 and 50 % for the exact discomfort level and between 80 and 85 % allowing deviations by one step. Our model quantifies driver discomfort. Such model may be included in the decision-making algorithms of active safety systems to improve driver acceptance. In fact, by tuning system activation times depending on the expected level of discomfort that a driver would experience in such situation, a system is not likely to annoy a driver.
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