Evidence for a fundamental property of steering

Benderius, Ola; Markkula, Gustav

doi:10.1177/1541931214581186

Cited by 26 publications

(21 citation statements)

References 14 publications

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“…However, the level of description used in Figure 2 is sufficient for assessing the impact of these components upon smooth and safe control transitions. Control (determining how rapidly, and by how much, to turn the steering wheel to produce the desired wheel angle, Benderius & Markkula, 2014) and Gaze Control (e.g. saccading to, then tracking, a waypoint on the future path then tracking this waypoint, Wann & Land, 2000;Wilkie & Wann, 2002 One characteristic of the loop shown in Figure 2 is that for steering actions to remain well-calibrated with respect to the environment, it will need to operate at a sufficiently high frequency.…”

Section: An Operational Control Loop That Reflects the Perceptual-motmentioning

confidence: 99%

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

Mole

Lappi

Giles³

et al. 2019

Hum Factors

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Objective: To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to automated vehicle (AV)–human transitions. Background: Manual control of vehicles is made possible by the coordination of perceptual-motor behaviors (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood. Method: We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarized as a perceptual-motor loop that requires (a) calibration and (b) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework. Results: The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored. Conclusion: Modeling the coordination of gaze and steering and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving. Application: This conclusion poses a challenge for future research on AV-human transitions. Future studies need to provide an understanding of human behavior that will be sufficient to capture the essential characteristics of drivers reengaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering and potential routes to improving manual control recovery.

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Section: An Operational Control Loop That Reflects the Perceptual-motmentioning

confidence: 99%

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

Mole

Lappi

Giles³

et al. 2019

Hum Factors

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“…Previously, it has been observed that amplitude and maximum steering rate of severe evasive maneuvers are linearly correlated (Breuer, 1998;Markkula et al, submitted), suggesting a constant maneuver duration. Recently, Benderius and Markkula (2014) have shown that this correlation exists also for routine steering adjustments. These were found to follow bell-shaped profiles of movement speed, similar to what is consistently observed in laboratory experiments on reaching (Franklin & Wolpert, 2011).…”

Section: Driving Control As a Series Of Open-loop Adjustmentsmentioning

confidence: 99%

Modeling driver control behavior in both routine and near-accident driving

Markkula

2014

Proceedings of the Human Factors and Ergonomics Society Annual

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Building on ideas from contemporary neuroscience, a framework is proposed in which drivers' steering and pedal behavior is modeled as a series of individual control adjustments, triggered after accumulation of sensory evidence for the need of an adjustment, or evidence that a previous or ongoing adjustment is not achieving the intended results. Example simulations are provided. Specifically, it is shown that evidence accumulation can account for previously unexplained variance in looming detection thresholds and brake onset timing. It is argued that the proposed framework resolves a discrepancy in the current driver modeling literature, by explaining not only the short-latency, well-tuned, closed-loop type of control of routine driving, but also the degradation into long-latency, ill-tuned open-loop control in more rare, unexpected, and urgent situations such as near-accidents.

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“…6 suggest that this is due to variations in the number of smaller steering corrections needed to achieve satisfactory collision avoidance (cf. [50]). It can also be noted that the average of 0.56 s shown in Fig.…”

Section: Open Loop Avoidance Steeringmentioning

confidence: 99%

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

Markkula

Benderius

Wahde

2014

Vehicle System Dynamics

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A number of driver models were fitted to a large data set of human truck driving, from a simulated near-crash, low-friction scenario, yielding two main insights: Steering to avoid a collision was best described as an open-loop manoeuvre of predetermined duration, but with situation-adapted amplitude, and subsequent vehicle stabilization could to a large extent be accounted for by a simple yaw rate nulling control law. These two phenomena, which could be hypothesized to generalize to passenger car driving, were found to determine the ability of four driver models adopted from literature to fit the human data. Based on the obtained results, it is argued that the concept of internal vehicle models may be less valuable when modelling driver behaviour in non-routine situations such as near-crashes, where behaviour may be better described as direct responses to salient perceptual cues. Some methodological issues in comparing and validating driver models are also discussed.

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Evidence for a fundamental property of steering

Cited by 26 publications

References 14 publications

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

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

Modeling driver control behavior in both routine and near-accident driving

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

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