Predictive shared steering control for driver override in automated driving: A simulator study

Guo, Chunshi; Sentouh, Chouki; Popieul, Jean‐Christophe; Haué, Jean-Baptiste

doi:10.1016/j.trf.2017.12.005

Cited by 34 publications

(19 citation statements)

References 24 publications

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“…One such measure is perceived ease of use (PEOU, [14]), which can be operationalized as a single item measure. PEOU has indeed been frequently used in automated vehicle research (e.g., [15][16][17][18][19][20][21][22][23]). The technology acceptance model [14] incorporates external factors (e.g., training), which directly influence PEOU.…”

Section: Validation and Development Of Methodologymentioning

confidence: 99%

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Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving

Forster

Hergeth

Naujoks

et al. 2019

MTI

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The development of automated driving will profit from an agreed-upon methodology to evaluate human–machine interfaces. The present study examines the role of feedback on interaction performance provided directly to participants when interacting with driving automation (i.e., perceived ease of use). In addition, the development of ratings itself over time and use case specificity were examined. In a driving simulator study, N = 55 participants completed several transitions between Society of Automotive Engineers (SAE) level 0, level 2, and level 3 automated driving. One half of the participants received feedback on their interaction performance immediately after each use case, while the other half did not. As expected, the results revealed that participants judged the interactions to become easier over time. However, a use case specificity was present, as transitions to L0 did not show effects over time. The role of feedback also depended on the respective use case. We observed more conservative evaluations when feedback was provided than when it was not. The present study supports the application of perceived ease of use as a diagnostic measure in interaction with automated driving. Evaluations of interfaces can benefit from supporting feedback to obtain more conservative results.

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Section: Validation and Development Of Methodologymentioning

confidence: 99%

“…In an applied context of HMI testing in user studies, there are many examples reporting high ease of use (see e.g., [15,20,22]) even in first contacts. These results certainly give an important insight into participants' subjective evaluation, namely that they perceive interaction as considerably effortless.…”

Section: Calibrating Effects Of Feedbackmentioning

confidence: 99%

Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving

Forster

Hergeth

Naujoks

et al. 2019

MTI

View full text Add to dashboard Cite

show abstract

“…Iwano et al [96] calculated the optimal torque based on a dynamic bicycle model with steering. Also, some authors propose the use of optimal control methods; Mars et al [53] applied an H 2 optimization technique while varying the degree of assistance, and Guo et al [103], [105] designed a constrained MPC for road departure prevention, with a variation of the LoHA dependent on driver effort, and lane change intention.…”

Section: A Model-free Coupled Shared Controllersmentioning

confidence: 99%

“…Many benefits of these systems have been reported: an improvement in tracking performance (e.g., lateral error) [75], [88]- [90]; a lower visual demand, enabling the possibility of performing secondary tasks while driving [75], [88], [89]; reduced driver effort (reductions in the steering reversal rate [79] and driver torque [62], [66], [96]); improved driving under low-visibility conditions [53]; an increase in the TLC [99]; a smooth control transition and greater stability when transferring authority [7], [102], [103]; and a decrease in the number of collisions during obstacle avoidance [81], [90]. One work showed the benefit of the haptic guidance in reducing passive driver fatigue [99], and the consideration of the four design aspects listed above has been shown to result in a significant reduction in driver-automation conflicts [78].…”

Section: A Model-free Coupled Shared Controllersmentioning

confidence: 99%

“…This has led to some drawbacks: drivers may misinterpret the feedback from a haptic system [88], respond by increasing their control efforts [75], and experience a feeling of being overwhelmed [14], [62]. Undesired steering conflicts [96] and no reduction in steering effort [103] have also been reported. For additional information on the effects of haptic guidance systems on automated driving, the reader is referred to Petermeijer's analysis [20], which includes specific data from some of the works presented here.…”

Section: A Model-free Coupled Shared Controllersmentioning

confidence: 99%

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A Review of Shared Control for Automated Vehicles: Theory and Applications

Marcano

Díaz

Pérez

et al. 2020

IEEE Trans. Human-Mach. Syst.

143

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The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory-and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driverin-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.

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Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems

et al. 2019

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Predictive shared steering control for driver override in automated driving: A simulator study

Cited by 34 publications

References 24 publications

Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving

Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving

A Review of Shared Control for Automated Vehicles: Theory and Applications

Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems

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