Impact of training and in-vehicle task performance on manual control recovery in an automated car

Payre, William; Cestac, Julien; Dang, Nguyen-Thong; Vienne, Fabrice; Delhomme, Patricia

doi:10.1016/j.trf.2017.02.001

Cited by 96 publications

(66 citation statements)

References 24 publications

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“…Previously, when an autonomous driving study was conducted, the participants were only briefly informed about the functioning and the interface of the car by the experimenter before starting the test. Recent work instead extensively investigates the impact of training and familiarization with autonomous driving and take-over requests [11]- [13]. Payre et al [11] analyzed the effectiveness of a training based on simple practice in the simulator with respect to a more elaborated training which included a text, a video and a more complex practice in the simulator.…”

Section: A Driver Interaction With Automated Vehiclesmentioning

confidence: 99%

“…Recent work instead extensively investigates the impact of training and familiarization with autonomous driving and take-over requests [11]- [13]. Payre et al [11] analyzed the effectiveness of a training based on simple practice in the simulator with respect to a more elaborated training which included a text, a video and a more complex practice in the simulator. Sportillo et al [12] compared three different training supports: a user manual displayed on a laptop, a fixedbase driving simulator and a Light Virtual Reality system.…”

Section: A Driver Interaction With Automated Vehiclesmentioning

confidence: 99%

See 1 more Smart Citation

On-Road Evaluation of Autonomous Driving Training

Sportillo¹,

Paljic²,

Ojeda³

2019

2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

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Driver interaction with increasingly automated vehicles requires prior knowledge of system capabilities, operational know-how to use novel car equipment and responsiveness to unpredictable situations. With the purpose of getting drivers ready for autonomous driving, in a between-subject study sixty inexperienced participants were trained with an on-board video tutorial, an Augmented Reality (AR) program and a Virtual Reality (VR) simulator. To evaluate the transfer of training to real driving scenarios, a test drive on public roads was conducted implementing, for the first time in these conditions, the Wizard of Oz (WoZ) protocol. Results suggest that VR and AR training can foster knowledge acquisition and improve reaction time performance in take-over requests. Moreover, participants' behavior during the test drive highlights the ecological validity of the experiment thanks to the effective implementation of the WoZ methodology.

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Section: A Driver Interaction With Automated Vehiclesmentioning

confidence: 99%

Section: A Driver Interaction With Automated Vehiclesmentioning

confidence: 99%

On-Road Evaluation of Autonomous Driving Training

Sportillo¹,

Paljic²,

Ojeda³

2019

2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

View full text Add to dashboard Cite

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“…The impact of peripheral visual information in alleviating motion sickness when engaging in nondriving tasks in fully automated driving (Karjanto et al, 2018) Driver response when experiencing a safety critical take-over request (Blommer et al, 2017;Merat et al, 2014b;Shen & Neyens, 2017;Vogelpohl et al, 2018;Zeeb et al, 2015); Necessary time for drivers to achieve situation awareness (Agrawal et al, 2017;Vlakveld et al, 2018); How situation awareness in transfer of control is affected by driver fatigue, stress, (Neubauer et al, 2012); by workload (de Winter et al, 2014); by mental task demands (Heikoop et al, 2018); by alert cue specificity (Forster et al, 2017;Wright et al, 2017); and by driver experience (Wright et al, 2016b); Resuming control from the automation (Bueno et al, 2016;Clark & Jing Feng 2017;Gold et al, 2015;Merat & Lee, 2012;Merat et al, 2014;Merat et al, 2014b;Naujoks et al, 2018;Navarro et al, 2016;Petermeijer et al, 2017;Payre et al, 2017;Radlmayr et al, 2014;Zeeb et al, 2017;Zhang et al, 2018); Post-transition driving performance (Berthelon & Gineyt, 2014;Miller et al, 2015;Mok et al, 2015;Naujoks et al, 2015;Naujoks, Purucker, et al 2017;Radlmayr et al, 2018;Strand et al, 2014;…”

Section: Introductionmentioning

confidence: 99%

Driving with and without automation on the motorway – an observational study

Várhelyi

Kaufmann

Johnsson

et al. 2020

Journal of Intelligent Transportation Systems

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User-related assessment of a level 3 automated driving system, providing functions such as lane-and distance-keeping, stop & go driving, lane change and overtaking, was carried out on a motorway in Germany with the aim to assess user-related issues of automated driving, i.e., behavior when driving with automation on motorways, user experiences, reactions and acceptance. Twenty-one persons drove twice along the test route once with the system switched off and once with the system active. Driving data were logged and driving behavior was observed by two observers in the car and the drivers answered questionnaires. The results revealed that the drivers used the system as it was intended to be used and that the system affected driving positively in several ways, resulting in better speed adaptation, less speed variation, better distance keeping, better lane choice, better indicator usage and fewer dangerous lane changes. No differences with regard to subjective workload were shown. The system was perceived as being both useful and satisfactory. However, it was found that the system did not react to other drivers' intention to make a lane change, especially in situations where they were attempting to merge onto the motorway. Further development is needed to improve the system's ability to recognize other vehicles' intention to enter the vehicle's own lane. Also, the phenomenon of the driver "feeling guilty" because of the system's reckless behavior by not allowing other drivers to merge onto the motorway or hindering other cars behind due to lengthy overtaking should be investigated further.

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“…Most of the studies in this domain implement safetycritical take-over scenarios caused by an obstacle (usually a 155 broken down vehicle) on the current lane [24,17,21,25,23] and non-critical scenarios caused by the absence of lane markings [24,26]. To ensure security and to succeed in the take-over process, it is important to understand how much time before a system boundary a driver who is out of the loop should be warned.…”

mentioning

confidence: 99%

“…Payre et. al [26] addressed the problem of drivers' training in an automated car by comparing two types of training: a simple training based only on practice in a driving simulator and an elaborated training which included a text, a tutorial video and a more elaborated practice in the simulator. They found that participants in the elaborated training group trusted more the automated driving and were able to take-over faster than those in the simple training group.…”

mentioning

confidence: 99%

Get ready for automated driving using Virtual Reality

Sportillo

Paljic

Ojeda

2018

Accident Analysis & Prevention

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In conditionally automated vehicles, drivers can engage in secondary activities while traveling to their destination. However, drivers are required to appropriately respond, in a limited amount of time, to a take-over request when the system reaches its functional boundaries. Interacting with the car in the proper way from the first ride is crucial for car and road safety in general. For this reason, it is necessary to train drivers in a risk-free environment by providing them the best practice to use these complex systems. In this context, Virtual Reality (VR) systems represent a promising training and learning tool to properly familiarize drivers with the automated vehicle and allow them to interact with the novel equipment involved. In addition, Head-Mounted Display (HMD)-based VR (light VR) would allow for the easy deployment of such training systems in driving schools or car dealerships. In this study, the effectiveness of a light Virtual Reality training program for acquiring interaction skills in automated cars was investigated. The effectiveness of this training was compared to a user manual and a fixed-base simulator with respect to both objective and self-reported measures. Sixty subjects were randomly assigned to one of the systems in which they went through a training phase followed by a test drive in a high-end driving simulator. Results show that the training system affects the take-over performances. Moreover, self-reported measures indicate that the light VR training is preferred with respect to the other systems. Finally, another important outcome of this research is the evidence that VR plays a strategic role in the definition of the set of metrics for profiling proper driver interaction with the automated vehicle.

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Impact of training and in-vehicle task performance on manual control recovery in an automated car

Cited by 96 publications

References 24 publications

On-Road Evaluation of Autonomous Driving Training

On-Road Evaluation of Autonomous Driving Training

Driving with and without automation on the motorway – an observational study

Get ready for automated driving using Virtual Reality

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