Measuring and modeling driver steering behavior: From compensatory tracking to curve driving

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.

Effects of Simulator Motion on Driver Steering Performance with Various Visual Degradations

“…5a, b). This is most clear in TT (black portion of the bars), which is a well-known effect of restricted preview [17], [18]. DR performance (gray portion of the bars in Fig.…”

“…Besides the TT and DR performance measures presented here, such multisines can be further exploited by explicitly estimating the control dynamics of multiple of the driver response blocks in Fig. 2 using system identification techniques [17]- [19], [21], [22]. Ultimately, modeling the identified driver dynamics can lead to an even better quantitative understanding of driver steering and cue utilization [12], [17], [19], similar as obtained for piloting tasks with motion feedback [10].…”

“…2 using system identification techniques [17]- [19], [21], [22]. Ultimately, modeling the identified driver dynamics can lead to an even better quantitative understanding of driver steering and cue utilization [12], [17], [19], similar as obtained for piloting tasks with motion feedback [10]. In fact, controltheoretic modeling of the driver's behavior is perhaps the only approach that we currently have available for actually quantifying the relative contributions of visual and physical motion feedback on the driver's steering behavior.…”

“…2. Feedforward control has been shown to be most important for TT, that is, for drivers to guide their vehicle along a certain desired trajectory y c over the road [8], [17]- [19]. The main role of the feedback channels is to minimize residual errors and to suppress the effects of external perturbations δ sd (DR), such as wind gust [7], [8], [10], [19].…”

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

“…The core assumption of level 2-4 AV systems is that humans can and will safely and rapidly take-over control of a moving vehicle. Depending on the situation, however, the human driver may be faced with a set of environmental and vehicle characteristics that are drastically different from when they T that are appropriate for the conditions is not well aligned with our current understanding of how humans perform highly dynamic active control tasks such as steering (see Lappi & Mole, 2018, Mulder et al, 2017 for recent reviews). Nor is it supported by ergonomics research into human rarely required (Molloy & Parasuraman, 1996).…”