Measuring and modeling driver steering behavior: From compensatory tracking to curve driving. (February 2019)

• Record Type: Journal Article
• Authors: van der El, Kasper; Pool, Daan M.; Mulder, Max
• Is Part Of: Transportation research. Volume 61(2019)
• Page Start: 337
• Page End: 346
• Publication Date: 2019-02
• Subjects: Curve driving -- Compensatory tracking -- Driver modeling -- Preview -- System identification; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2017.09.011
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: A method is proposed to develop a novel driver model for curve driving. The approach extends well-known and often-applied models for compensatory tracking. Key innovations are preview, perspective, multiple feedbacks and boundary avoidance. Multiloop system identification is used to disentangle multiple driver responses. Initial human-in-the-loop data validates the feasibility of the proposed approach. Abstract: Drivers rely on a variety of cues from different modalities while steering, but which exact cues are most important and how these different cues are used is still mostly unclear. The goal of our research project is to increase understanding of driver steering behavior; through a measuring and modeling approach we aim to extend the validity of McRuer et al.'s crossover model for compensatory tracking to curve driving tasks. As part of this larger research project, this paper first analyzes the four main differences between compensatory tracking and curve driving: (1) pursuit and preview, (2) viewing perspective, (3) multiple feedback cues, and (4) boundary-avoidance strategies due to available lane width. Second, this paper introduces multiloop system identification as a method for explicitly disentangling the driver's simultaneous responses to various cues, which is subsequently applied to two sets of human-in-the-loop experimental data from a preview tracking and a curve driving experiment. The results suggest that recent human modeling advances for preview tracking can be extended to curve driving, by including the human's adaptation to viewing perspective, multiple feedback cues, and lane width. Such a model's physically interpretable parameters promise to provide unmatched insights into between-driver steering variations, and facilitate the systematic design of novel individualized driver support systems.