A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres. (November 2020)

• Record Type: Journal Article
• Authors: Kovaceva, Jordanka; Bärgman, Jonas; Dozza, Marco
• Is Part Of: Transportation research. Volume 75(2021)
• Page Start: 87
• Page End: 105
• Publication Date: 2020-11
• Subjects: Driver behaviour -- Overtaking -- Cyclist -- Driver modelling -- Linear program; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2020.09.020
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: We modelled the time when drivers start steering away to overtake a cyclist. Four driver models were compared using test-track and naturalistic data. Modelling inverse tau fitted the data better than modelling expansion rate. The compared models have implications for evaluation of active safety systems. Abstract: The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions. This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver's retina) and inverse tau (the ratio between the image's expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming. The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT. The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems.