Supporting the changing driver’s task: Exploration of interface designs for supervision and intervention in automated driving

Beukel, Arie Paul van den; Voort, Mascha C. van der; Eger, Arthur O.

doi:10.1016/j.trf.2016.09.009

Cited by 34 publications

(27 citation statements)

References 70 publications

(111 reference statements)

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“…Finally, it is noted that a number of studies have used the concept of "soft-TOR" or "two-step TOR" to acquire the driver's attention before taking over control (Lapoehn et al, 2016;Naujoks, Purucker, Neukum, Wolter, & Steiger, 2015;Van den Beukel, van der Voort, & Eger, 2016;Willemsen, Stuiver, & Hogema, 2015; and see Brandenburg & Epple, 2018 for a questionnaire study). Two-step TORs differ from MRs because with a two-step TOR, the driver always has to take over after receiving the notification, whereas this is not necessarily the case with the MR concept.…”

Section: Monitoring Requests and Uncertainty Presentationmentioning

confidence: 99%

Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance

Zhang

Feldhütter

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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In conditionally automated driving, drivers do not have to monitor the road, whereas in partially automated driving, drivers have to monitor the road permanently. We evaluated a dynamic allocation of monitoring tasks to human and automation by providing a monitoring request (MR) before a possible takeover request (TOR), with the aim to better prepare drivers to take over safely and efficiently. In a simulator-based study, an MR+TOR condition was compared with a TOR-only condition using a within-subject design with 41 participants. In the MR+TOR condition, an MR was triggered 12 s before a zebra crossing, and a TOR was provided 7 s after the MR onset if pedestrians crossing the road were detected. In the TOR-only condition, a TOR was provided 5 s before the vehicle would collide with a pedestrian if the participant did not intervene. Participants were instructed to perform a self-paced visual-motor non-driving task during automated driving. Eye tracking results showed that participants in the MR+TOR condition responded to the MR by looking at the driving environment. They also exhibited better takeover performance, with a shorter response time to the TOR and a longer minimum time to collision as compared to the TOR-only condition. Subjective evaluations also showed advantages of the MR: participants reported lower workload, higher acceptance, and higher trust in the MR+TOR condition as compared to the TOR-only condition. Participants' reliance on automation was tested in a third drive (MR-only condition), where automation failed to provide a TOR after an MR. The MR-only condition resulted in later responses (and errors of omission) as compared to the MR+TOR condition. It is concluded that MRs have the potential to increase safety and acceptance of automated driving as compared to systems only providing TORs. Drivers' trust calibration and reliance on automation will need further investigation.

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Section: Monitoring Requests and Uncertainty Presentationmentioning

confidence: 99%

Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance

Zhang

Feldhütter

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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“…In a prior meta-analysis, a greater degree of automation was found to be associated with reduced ability to recover from a system failure (Onnasch et al, 2014). Importantly, increased levels of vehicle automation shift the driver’s role from one of active control to one of a supervisor of the automation (van den Beukel et al, 2016). It is imperative to understand how advanced vehicle automation affects the safety of drivers and passengers.…”

Section: Introductionmentioning

confidence: 99%

Comparing the Relative Strengths of EEG and Low-Cost Physiological Devices in Modeling Attention Allocation in Semiautonomous Vehicles

Cisler

Greenwood

Roberts

et al. 2019

Front. Hum. Neurosci.

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As semiautonomous driving systems are becoming prevalent in late model vehicles, it is important to understand how such systems affect driver attention. This study investigated whether measures from low-cost devices monitoring peripheral physiological state were comparable to standard EEG in predicting lapses in attention to system failures. Twenty-five participants were equipped with a low-fidelity eye-tracker and heart rate monitor and with a high-fidelity NuAmps 32-channel quick-gel EEG system and asked to detect the presence of potential system failure while engaged in a fully autonomous lane changing driving task. To encourage participant attention to the road and to assess engagement in the lane changing task, participants were required to: (a) answer questions about that task; and (b) keep a running count of the type and number of billboards presented throughout the driving task. Linear mixed effects analyses were conducted to model the latency of responses reaction time (RT) to automation signals using the physiological metrics and time period. Alpha-band activity at the midline parietal region in conjunction with heart rate variability (HRV) was important in modeling RT over time. Results suggest that current low-fidelity technologies are not sensitive enough by themselves to reliably model RT to critical signals. However, that HRV interacted with EEG to significantly model RT points to the importance of further developing heart rate metrics for use in environments where it is not practical to use EEG.

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“…The most important drawbacks include the necessity for additional hardware, the high demand on the CPU load for data processing, the cost, susceptibility to environmental effects, and the need for driver inputs. Also, the studies are often limited to emergency/hazardous situations [20], [27], [28], vehicle initiated take-over requests [18], [19], [29], [30], or focused on driver state monitoring [6], [13], [31].…”

Section: Introductionmentioning

confidence: 99%

Driver Intervention Detection via Real-Time Transfer Function Estimation

Schinkel

Sande

Nijmeijer

2021

IEEE Trans. Intell. Transport. Syst.

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Currently, the driver plays a crucial role in the safety of autonomous vehicles, functioning as the fall back for vehicle control systems. Fast and accurate detection of driver intervention is therefore of importance. In this article a novel driver intervention detection method for automated vehicles is presented and tested. Non-critical transitions are considered, excluding safety related applications. The transfer function between the electric power steering torque and steering column angle is estimated by perturbing the steering system with a known disturbance. This estimated value is used to detect whether a driver is intervening. The detection algorithm has been tested in simulations using a four degree-of-freedom vehicle model. The parameters of this vehicle model have been obtained via frequency response measurements performed on a test vehicle. Secondly, the performance of the algorithm has been tested with on-road measurements. The results show that driver intervention can be successfully detected within 0.4 seconds. The performance in terms of true and false detections has been analyzed, and the presented solution is shown to be robust to measurement noise and road disturbances.

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Supporting the changing driver’s task: Exploration of interface designs for supervision and intervention in automated driving

Cited by 34 publications

References 70 publications

Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance

Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance

Comparing the Relative Strengths of EEG and Low-Cost Physiological Devices in Modeling Attention Allocation in Semiautonomous Vehicles

Driver Intervention Detection via Real-Time Transfer Function Estimation

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