scholarly journals The Effect of Takeover Lead Time on Driver Workload

2019 ◽  
Vol 63 (1) ◽  
pp. 1872-1873
Author(s):  
Yongdeok Yun ◽  
Hyungseok Oh ◽  
Rohae Myung
Keyword(s):  
False Alarm ◽  
Lead Time ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Physiological Signals ◽  
Sufficient Time ◽  
Subjective Workload ◽  
Driver Workload ◽  
Level 3 ◽  
Driving Task

Autonomous driving has been one of the most interesting technologies in recent years with expectation of solving accidents, pollution, and traffic jams (Jo, Lee, & Kim, 2013; Schrank, Eisele, & Lomax, 2012; Singh, 2018). However, current autonomous vehicles cannot handle all driving situations. Therefore, drivers must intervene in certain situations. SAE international defined these levels of autonomous driving as partial (level 2) and conditional (level 3) autonomous driving (SAE international, 2016). In level 3 autonomous driving, drivers are not required to monitor the driving situations and they may perform non-driving related tasks (NDRTs). However, drivers must pay attention to driving situations and make an appropriate reaction when takeover request (TOR) occurs. Takeover request (TOR) is one of the major issues in autonomous driving. A human driver must be ready to transfer the control of the vehicle when TOR is given. Therefore, how and when to request the driver to transfer the control is important. In this regard, takeover lead time (TORlt) has received great attention and there are many existing scholarly works on the effect of TORlt on takeover performance (Gold et al., 2013; Gold et al., 2017; Mok, Johns, Lee, Ive et al., 2015; Mok Johns Lee, Miller et al., 2015; Payre et al., 2016; Van den Beukel & Van der Voort, 2013; Wan & Wu, 2018; Zhang et al., 2018). Besides its impact on takeover performance, TORlt also has an effect on driver workload (Eriksson & Stanton, 2017; Wan & Wu, 2018). Inappropriate TORlt makes driver overload or underload and the abnormal workload deteriorates driver performance in takeover situation (De Winter et al., 2014; Eriksson & Stanton, 2017; Hajek et al., 2013; Wan & Wu, 2018). However, these studies either did not investigate workload induced by TOR or measure driver workload in a subjective method. This study focused on workload induced by TOR. Wan & Wu (2018) stated that takeover request without sufficient time budget may increase driver workload and generate erratic driver's response. However, many researches have focused on workload while performing NDRT alone. Additionally, a few research that assessed workload induced by TOR used subjective methods. The objective of this study is to investigate the effects of TORlt on driver workload in takeover situation. This study hypothesizes that workload would increase when TORlt is too short or too long. To investigate the hypothesis, an experiment was conducted with driving simulator and workload was measured by subjective and objective methods. The experiment with driving simulator was conducted with 28 participants to investigate the effect of TORlt on the driver workload. TORlt was controlled in 7 levels (3s, 7s, 10s, 15s, 30s, 45s, 60s). Each session of the experiment was dealt with one TORlt level and was conducted in random sequence. At the beginning of the session, participants had to perform the NDRT during autonomous driving. Then, they are required to identify an obstacle in ego-lane and make a lane change to avoid the collision when TOR occurs. The dependent variables in this experiment include workload measured by subjective and physiological methods. Driving Activity Load Index (DALI; Pauzie, 2008) was conducted to measure subjective workload and physiological measures including respiration rate (RSP), heart rate (HR), and galvanic skin reponse (GSR) were conducted to evaluate objective workload. The results of this study showed that TORlt had a significant effect on subjective workload. Subjective workload was increased in short TORlt as expected. Drivers, who were given the TOR with short lead time, did not have sufficient time to perceive and comprehend the driving situation nor make an appropriate decision. As a result, drivers could not cope with the takeover situation and their workload increased. However, driver workload was not increased in excessively long TORlt. Long TORlt was expected to increase driver workload because driver could assume long TORlt to be a false alarm or feel distraction (Wan & Wu, 2018). This might be because participants did not consider that 60s of TORlt was long or there was no false alarm in the experiment. There was no significant effect of TORlt on mean RSP and mean HR. This is because each participant behaved differently or regarded driving situation after the takeover as a simple driving task. In contrast to RSP and HR, TORlt had a significant effect on mean GSR. According to Kramer (1991), Physiological signals are sensitive to different resource demands (Kramer, 1991; Ryu & Myung, 2005). In this study, excessive temporal demand because of short TORlt and distraction caused by long TORlt were demands imposed on the participants. Hence, GSR which is sensitive to emotion and frustration (Kramer, 1991) was influenced by TORlt. In conclusion, the results of the study were different from the hypothesis which expected excessive workload with too short or long TORlt. Even though subjective workload and GSR partially support the hypothesis, more complicated and controlled experiment is needed to confirm the hypothesis. In the future research, experiment including complex driving task and false alarm should be conducted and it is necessary to measure physiological signals while controlling various resource demand to investigate the effect of TORlt on physiological signals.

The Effects of Continuous Driving-Related Feedback on Drivers’ Response to Automation Failures

2017 ◽  
Vol 61 (1) ◽  
pp. 1980-1984 ◽  
Author(s):  
Guy Cohen-Lazry ◽  
Avinoam Borowsky ◽  
Tal Oron-Gilad
Keyword(s):  
Driving Simulator ◽  
Reaction Times ◽  
Autonomous Driving ◽  
The Other ◽  
Control Group ◽  
Driving Situation ◽  
Failure Event ◽  
Level 3 ◽  
Driving Task ◽  
Continuous Feedback

During prolonged periods of autonomous driving, drivers tend to shift their attention away from the driving task. As a result, they require more time to regain awareness of the driving situation and to react to it. This study examined the use of informative automation that during Level-3 autonomous driving provided drivers with continuous feedback regarding the vehicle’s actions and surroundings. It was hypothesized that the operation of informative automation will trigger drivers to allocate more attention to the driving task and will improve their reaction times when resuming control of the vehicle. Sixteen participants drove manual and autonomous driving segments in a driving simulator equipped with Level-3 automation. For half of the participants, the informative automation issued alerts and messages while for the other half no messages were issued (control). The number of on-road glances served as a proxy for drivers’ attention. Drivers’ performance on handling an unexpected automation failure event was measured using their time-to-brake and time-to-steer. Results showed that drivers using the informative automation made more frequent on-road glances than drivers in the control group. Yet, there were no significant differences in reaction times to the automation failure event between the groups. Explanations and implications of these results are discussed.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

Attentive User Interfaces to Improve Multitasking and Take-Over Performance in Automated Driving

2019 ◽  
Vol 11 (3) ◽  
pp. 40-58 ◽  
Author(s):  
Philipp Wintersberger ◽  
Clemens Schartmüller ◽  
Andreas Riener
Keyword(s):  
User Interfaces ◽  
Lead Time ◽  
Driving Simulator ◽  
Handheld Devices ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Level 3 ◽  
Traffic Systems ◽  
Consumer Devices ◽  
Positive Effect

Automated vehicles promise engagement in side activities, but demand drivers to resume vehicle control in Take-Over situations. This pattern of alternating tasks thus becomes an issue of sequential multitasking, and it is evident that random interruptions result in a performance drop and are further a source of stress/anxiety. To counteract such drawbacks, this article presents an attention-aware architecture for the integration of consumer devices in level-3/4 vehicles and traffic systems. The proposed solution can increase the lead time for transitions, which is useful to determine suitable timings (e.g., between tasks/subtasks) for interruptions in vehicles. Further, it allows responding to Take-Over-Requests directly on handheld devices in emergencies. Different aspects of the Attentive User Interface (AUI) concept were evaluated in two driving simulator studies. Results, mainly based on Take-Over performance and physiological measurements, confirm the positive effect of AUIs on safety and comfort. Consequently, AUIs should be implemented in future automated vehicles.

scholarly journals Human-Centered Intervention Based on Tactical-Level Input in Unscheduled Takeover Scenarios for Highly-Automated Vehicles

2019 ◽  
Vol 18 (3) ◽  
pp. 451-460
Author(s):  
Mitsuhiro Kamezaki ◽  
Hiroaki Hayashi ◽  
Udara E. Manawadu ◽  
Shigeki Sugano
Keyword(s):  
Driving Simulator ◽  
Situational Awareness ◽  
Functional Limitations ◽  
Hand Gesture ◽  
Haptic Interfaces ◽  
Automated Vehicles ◽  
Operating Level ◽  
Driver Workload ◽  
Level 3 ◽  
Control Authority

AbstractDue to functional limitations in certain situations, the driver receives a request to intervene from automated vehicles operating level 3. Unscheduled intervention of control authority would lead to insufficient situational awareness, then this will make dangerous situations. The purpose of this study is thus to propose tactical-level input (TLI) method with a multimodal driver-vehicle interface (DVI) for the human-centered intervention. The proposed DVI system includes touchscreen, hand-gesture, and haptic interfaces that enable interaction between driver and vehicle, and TLI along with such DVI system can enhance situational awareness. We performed unscheduled takeover experiments using a driving simulator to evaluate the proposed intervention system. The experimental results indicate that TLI can reduce reaction time and driver workload, and moreover, most drivers preferred the use of TLI than manual takeover.

scholarly journals Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority

2020 ◽  
pp. 001872082094618 ◽  
Author(s):  
Davide Maggi ◽  
Richard Romano ◽  
Oliver Carsten
Keyword(s):  
Road Safety ◽  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Safety Issues ◽  
The Road ◽  
Operational Design ◽  
Level 3 ◽  
Driving Task

Objective A driving simulator study explored how drivers behaved depending on their initial role during transitions between highly automated driving (HAD) and longitudinally assisted driving (via adaptive cruise control). Background During HAD, drivers might issue a take-over request (TOR), initiating a transition of control that was not planned. Understanding how drivers behave in this situation and, ultimately, the implications on road safety is of paramount importance. Method Sixteen participants were recruited for this study and performed transitions of control between HAD and longitudinally assisted driving in a driving simulator. While comparing how drivers behaved depending on whether or not they were the initiators, different handover strategies were presented to analyze how drivers adapted to variations in the authority level they were granted at various stages of the transitions. Results Whenever they initiated the transition, drivers were more engaged with the driving task and less prone to follow the guidance of the proposed strategies. Moreover, initiating a transition and having the highest authority share during the handover made the drivers more engaged with the driving task and attentive toward the road. Conclusion Handover strategies that retained a larger authority share were more effective whenever the automation initiated the transition. Under driver-initiated transitions, reducing drivers’ authority was detrimental for both performance and comfort. Application As the operational design domain of automated vehicles (Society of Automotive Engineers [SAE] Level 3/4) expands, the drivers might very well fight boredom by taking over spontaneously, introducing safety issues so far not considered but nevertheless very important.

scholarly journals AmbientBreath

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
Jinmo Lee ◽  
Neska Elhaouij ◽  
Rosalind Picard
Keyword(s):  
Background Noise ◽  
Sensory Feedback ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
User Acceptance ◽  
Driving Safety ◽  
Just In Time ◽  
Breathing Rate ◽  
Safety Measures ◽  
Driving Task

To promote calm breathing inside a car, we designed a just-in-time breathing intervention stimulated by multi-sensory feedback and evaluated its efficacy in a driving simulator. Efficacy was measured via reduction in breathing rate as well as by user acceptance and driving safety measures. Drivers were first exposed to demonstrations of three kinds of ambient feedback designed to stimulate a goal breathing rate: (1) auditory (rhythmic background noise), (2) synchronized modulation of wind (dashboard fans modulating air pointed toward the driver) together with auditory, or (3) synchronized visual (ambient lights) together with auditory. After choosing one preference from these three, each driver engaged in a challenging driving task in a car simulator, where the ambient stimulation was triggered when their breathing exceeded a goal rate adapted to their personal baseline. Two user studies were conducted in a car simulator involving respectively 23 and 31 participants. The studies include both manual and autonomous driving scenarios to evaluate drivers' engagement in the intervention under different cognitive loads. The most frequently selected stimulation was the combined auditory and wind modalities. Measures of changes in breathing rate show that the participants were able to successfully engage in the breathing intervention; however, several factors from the driving context appear to have an impact on when the intervention is or is not effective.

scholarly journals Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

2018 ◽  
Vol 2 (4) ◽  
pp. 68 ◽  
Author(s):  
Natalie T. Richardson ◽  
Lukas Flohr ◽  
Britta Michel
Keyword(s):  
Driving Simulator ◽  
Situational Awareness ◽  
Automated Driving ◽  
Human Machine Interaction ◽  
Transport Efficiency ◽  
Additional Information ◽  
Level 3 ◽  
Driving Task ◽  
Machine Interface ◽  
Vehicle Automation

Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

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