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Purpose – Level 3 automated driving, which has been defined by the Society of Automotive Engineers, may cause driver drowsiness or lack of situation awareness, which can make it difficult for the driver to recognize where he/she is. Therefore, the purpose of this study was to conduct an experimental study with a driving simulator to investigate whether automated driving affects the driver’s own localization compared to manual driving. Design/methodology/approach – Seventeen drivers were divided into the automated operation group and manual operation group. Drivers in each group were instructed to travel along the expressway and proceed to the specified destinations. The automated operation group was forced to select a course after receiving a Request to Intervene (RtI) from an automated driving system. Findings – A driver who used the automated operation system tended to not take over the driving operation correctly when a lane change is immediately required after the RtI. Originality/value – This is a fundamental research that examined how the automated driving operation affects the driver's own localization. The experimental results suggest that it is not enough to simply issue an RtI, and it is necessary to tell the driver what kind of circumstances he/she is in and what they should do next through the HMI. This conclusion can be taken into consideration for engineers who design automatic driving vehicles. Keywords Automated vehicles, Autonomous driving, Advanced driver assistant systems, Driver behaviors and assistance, Human–machine interfaces, Request to intervene Paper type Research paper during L3 driving. However, some circumstances (e.g. 1. Introduction sensor failures, misunderstanding marked lanes) can make Automated vehicles are now in the spotlight. Automated L2 and L3 systems reach their limits; then, the system issues vehicle technology has raised expectations for the reduction a Request to Intervene (RtI). At this time, the driver should of human error while driving (Brookhuis et al., 2001)and take manual control of the vehicle immediately and may release people from the boring and distressing task of appropriately. driving (Stanton and Young, 2005), enhancing trafficsafety Although driving automation may promise such benefits, and fuel economy (Fagnant and Kockelman, 2015). The several human driver-related issues have been raised with five levels of autonomous driving as defined by the Society regard to automated driving systems. Some researchers of Automotive Engineers are widely accepted in the reported that automated driving can cause drivers to fall asleep automotive industry (SAE, 2016). Nowadays, research on compared to manual driving. Also, Naujoks et al. (2016) automated driving has mainly focused on the practical use of pointed out that drivers are more likely to pay attention to non- partial (Level 2; L2) and conditional (Level 3; L3) driving tasks while driving with adaptive cruise control (ACC) automated driving systems. In the partial automated driving and lane keeping assist (LKA). These matters may cause such system (L2) that executes both lateral and longitudinal controls on behalf of the human driver, the driver is required to continuously monitor the road environment and status of © Ryuichi Umeno, Makoto Itoh and Satoshi Kitazaki. Published in Journal the automated system. The conditional automated driving of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited. This article is published under the Creative Commons system (L3) executes not only vehicle dynamic controls but Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, also monitors surroundings. This means that the human translate and create derivative works of this article (for both commercial driver does not need to monitor the driving environment and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on This work was supported by Council for Science, Technology and Emerald Insight at: www.emeraldinsight.com/2399-9802.htm Innovation (CSTI), Crossministerial Strategic Innovation Promotion Program (SIP), entitled “Human Factors and HMI Research for Automated Driving”. Journal of Intelligent and Connected Vehicles Received 13 August 2018 1/3 (2018) 99–106 Revised 14 October 2018 Emerald Publishing Limited [ISSN 2399-9802] [DOI 10.1108/JICV-08-2018-0006] Accepted 22 October 2018 99 Influence of automated driving Journal of Intelligent and Connected Vehicles Ryuichi Umeno, Makoto Itoh and Satoshi Kitazaki Volume 1 · Number 3 · 2018 · 99–106 as out-of-the-loop (OoTL) performance problem (Endsley and 2. Method Kiris, 1995) where drivers away from control loops cannot 2.1 Participants adequately respond to system errors. This is partly due to lack Seventeen elderly participants (men, 12; women, 5), of age 66 to of an operator’s situation awareness. Merat and Jamson (2009) 82 years (M = 72.0, SD = 4.0), participated in the experiment. investigated the awareness and comprehension of driver’s The participants were recruited via a local human resource peripheral traffic during manual driving and automated agency. All of them hold a valid driver’s license and drove daily. driving, and as a result, under automated driving, the driver’s The experiment was conducted after obtaining approval from the response to the dangerous events will be delayed. In addition, University of Tsukuba Research Ethics Committee. there are several studies that show automated driving causes driver drowsiness (Jamson et al.,2013; De Winter et al.,2014), 2.2 Experimental design which will lead to a lack of situation awareness of driver. In the experiment, one factor in the “existence or nonexistence of Many studies have also been conducted on driver response to automated driving” was taken into consideration. It was a between- an RtI under different circumstances. Naujoks et al. (2014) subject design with two levels of autonomous operation (AO) and focused on the modality of RtI and reported that a driver manual operation (MO). The participants were randomly assigned responded to an RtI sooner if visual–auditory RtI was used to each group. The average age and composition of each group instead of just visual RtI. Zeeb et al. (2016) pointed out that the were as follows (AO: M= 73.6, men = 6, women = 3; MO: M= quality of the takeover can be attributed to driver behavior 71.3, men = 6, women = 2). before the RtI. Louw et al. (2017) proposed that human – machine interface (HMI) design should focus on helping the 2.3 Driving simulator driver take evasive action quickly rather than emphasizing the As presented in Figure 1, the experiment was conducted with a time to takeover. Most studies have focused on the time span motion-based driving simulator (Honda Motor Co., Ltd.). The and driver behavior required for an RtI. However, there are simulator has three LCD monitors that display the left and right other issues to consider with regard to RtI. Current vehicles are mirror image and rearview mirror image. The front field-of-view equipped with assorted features. For instance, car navigation was approximately 120° horizontally and 45° vertically. Two systems are installed in most cars and will tell the driver the LCD monitors for displaying the HMI (automated status) and current location and destination. However, the driver may be Internet TV (AbemaTV: https://abema.tv/)were mounted on deeply involved in non-driving task or be absentminded, a the dashboard (Figure 2). We used the TV programs of result based on past studies. Such a driver may not consciously AbemaTV with their permission. The simulator provided vehicle look at the car navigation system and confirm the current traveling sounds. The sounds were not too high to enable the location. This situation is not a huge problem as long as an automated driving system is performed without any Figure 1 Driving simulator used in the experiment abnormality. What can be a problem is when a tight RtI is issued. If the time margin given to the driver is small, or the tasks required for the drive is large, such as changing the driving lane after an RtI, the driver may not be able to immediately grasp the situation. Gold et al. (2013) revealed that it takes more than 5 s for the driver to respond to an Rtl owing to (the lack of or decline in) situation awareness. Thus, it is important to investigate the vehicle design such that the driver can grasp the current situation correctly in a limited time. Also, HMI will play a major role when and RtI is issued. Louw et al. (2017) proposed that HMI design should focus on helping the driver take evasive action quickly rather than emphasizing the time to takeover. In L3 automated driving, drivers do not need to do drive tasks and have a supervisory control of the system and environment. As a result, it is possible that the driver will not be Figure 2 View from the driver's seat of the driving simulator able to identify his/her location and does not know which direction to go in a limited time. In fact, in our past research, we confirmed a branch failed case despite presenting the correct branch lane after an RtI to the driver and the driver was able to grasp the steering wheel (NEDO, 2018). It is necessary to further investigate whether this result was caused by automated driving. Thus, the aim of this study is to investigate the influence of automated driving on the driver’s localization. In this study, driver’s localization means the ability to grasp what position on the route a car is currently travelling to the destination. As HMI design is as an important concern for elderly driving performance (Freund et al., 2005; Körber et al., 2016), this driving simulator study is targeted to elderly drivers. 100 Influence of automated driving Journal of Intelligent and Connected Vehicles Ryuichi Umeno, Makoto Itoh and Satoshi Kitazaki Volume 1 · Number 3 · 2018 · 99–106 drivers to hear the TV and an RtI request. To prevent motion Figure 4 Outline map of the driving course sickness, the motion function was not activated. 2.4 Automated driving system The automated driving system can be activated by pressing a button. The system maintained the vehicle’s speed at 70 km/h and kept the vehicle at the center of the driving lane. Note that an automatic lane-changing function was not installed. Drivers did not have to hold the steering wheel during autonomous operation. Drivers could disengage the autonomous operation by steadily pushing the brake pedal more than 30 per cent the amount of depression or moving the steering wheel more than the absolute value of 30°. 2.5 Human–machine interface The HMI was used to display automation status using an 8-inch of grace time before branching. Because the Tochigi-Tsuga JCT LCD monitor (Figure 2). When automation was activated, a had a large left curve before branching, an RtI was issued in a green image appeared; when deactivated, an orange image with straight section 250 m before branch considering the smooth an “off” sign appeared, as shown in Figure 3(a) and 3(b), handover. The scenarios were as follows (Figures 4 and 5): respectively. Figure 3(c) shows that the automated driving get off the expressway at Tsuga IC; system required the driver to resume control of the vehicle. The proceed to the right of Tochigi-Tsuga JCT; and RtI was issued with two consecutive beeping sounds. Even if the proceed to the left of Tochigi-Tsuga JCT. driver did not take any action following the RtI, the system was deactivated 10 s later. 2.7 Procedure Each participant signed an informed consent form after 2.6 Driving task receiving an explanation of the experiment’s purpose and All participants were instructed to proceed to a junction or a overview. A practice drive was given to each participant to get branch on an expressway specified in advance by an used to the simulator (approximately 5 min). Subsequently, the experimenter. They were also asked to drive safely. In addition, AO participants received an explanation about the automated the MO participants were asked to keep the vehicle's speed at system and HMI, and practiced automated driving for approximately 70 km/h and stay in the left lane (this is the approximately 5 min. They also performed exercises on how to driving lane in Japan). The AO participants were asked to engage and disengage the automated system by pressing the watch Internet TV during automated driving and to resume brake pedal or moving the steering wheel. control of the vehicle as needed. Afterwards, each participant drove under three scenarios. The experimental driving course was based on a section of Before each driving scenario, they received instructions from the Kita-Kanto expressway in Japan (Figure 4). It has two lanes the experimenter about the route using a map. There were no on each side and no other car existed. The starting point was navigation devices to indicate the vehicle's location and route to the same in all scenarios, which was the Kaminokawa IC. Since participants while driving. Thus, they had to remember the the automated driving system of this experiment operated only destination. a single lane, and it was impossible to change lanes Figure 5 presents a detailed outline of each scenario with an automatically. Thus, in the AO, the RtI request was issued actual simulator photo and illustrations. It took approximately approximately 200 m before the Tsuga IC exit, 250 m before 15 to 20 min to complete each driving scenario, and the order Tochigi-Tsuga JCT. This means that because the car was of drives was randomized for each participant. All participants travelling at 70 km/h, it gave the driver approximately 10 to 13 s took a 10-min break in between each drive. After all the drives, the participants were interviewed and took the Trail Making Test A/B (TMT A/B)(Tombaugh, 2004). TMT is a kind of Figure 3 (a) Status of the automation engaged; (b) status of the cognitive function test used to confirm whether participants automation disengaged and (c) issue of RtI (was written in Japanese) were assigned uniformly to each group. The total time of this experiment per participant was within 2 h. 2.8 Dependent measures To investigate our hypothesis, i.e. drivers may have difficulty in appropriately driving to the instructed destination during automation compared to manual operation, the data collected from the simulator were analyzed with following dependent measures. Results of the TMT A/B. This was used as supplemental data to confirm whether participants were equally allocated to each group. In this study, a significant decline in cognitive function 101 Influence of automated driving Journal of Intelligent and Connected Vehicles Ryuichi Umeno, Makoto Itoh and Satoshi Kitazaki Volume 1 · Number 3 · 2018 · 99–106 Figure 5 Detailed scenarios may cause differences in results because participants were Figure 6 Results of elapsed time for TMT-A tasked with memorizing their destination. Thus, it is necessary to ensure that the experimental results are reliable. Number of participants who drove appropriately to the instructed destination. For each driving condition, all participants experienced three scenarios. Whether they drove to the instructed direction was counted to investigate the driver’s ability to understand the location of the host vehicle. Time elapsed to hold the steering wheel. This is the elapsed time from when the system asked the driver to take control of the vehicle until the driver takes hold of the steering wheel in the AO condition. To investigate why the driver failed to reach the destination, the elapsed time was calculated. Point where lane change began. This indicates how far before the lane change was made from the branch point in Scenario B, in which drivers needed to change lanes to the right. This was used as an indicator to show how much the driver’s maneuvering attitude against branching differs between the AO and the MO conditions. Figure 7 Results of elapsed time for TMT-B In this experiment, we focused on the driver’s ability to grasp driver’s own location, and thus we will analyze the precise analysis of vehicle behavior as necessary in the future. 3. Results 3.1 Results of TMT-A/B The results of TMT-A and TMT-B are shown in Figures 6 and 7, respectively. Error bars indicate the standard deviation. The result of one-factor analysis of variance (ANOVA) on the “existence or nonexistence of automated driving” showsthatthe main effect was not significant [TMT-A: F(1,8) = 5.32, p > 0.05, TMT-B: F(1,8) = 2.81, p > 0.05]. Therefore, it can be considered that 102 Influence of automated driving Journal of Intelligent and Connected Vehicles Ryuichi Umeno, Makoto Itoh and Satoshi Kitazaki Volume 1 · Number 3 · 2018 · 99–106 participants were homogeneous between the two groups and the Figure 9 Time for grasping the steering experimental results are worthy of analysis. 3.2 Driving appropriately to the instructed direction Figure 8 shows “the proportion of drivers who proceeded to the appropriate course. Between groups, a Friedman test of success rate provided a chi-square value of 0.33, which was not significant (p > 0.1). In the MO, the ratio is almost equal in each scenario. On the other hand, approximately half of the AO participants failed only in Scenario B (to proceed to the right of the JCT), but had no errors in the other scenarios. A chi-square test of the success rate between Scenarios A-B and B-C in the AO provided a chi-square value of 2.89, which was marginally significant (p < 0.1). Although branch failures were certainly confirmed even in MO, an approximately half of AO drivers failed in Scenario B where a driver should proceed to the right lane. The driver may be able to properly deal with an RtI in the case of going straight to the left or leaving to the left lane like Scenarios A and C. On the other hand, in Scenario B where lane change was required to the right lane after an RtI, the driver may have proceeded to the left lane as they were trying to grasp the situation. Despite the relatively long grace period after an RtI, it is noteworthy that some drivers did not proceed to correct lane. This matter may have been more significant in more critical situations. Failure to do the appropriate lane Figure 9 and Table I, half of the participants gripped the change may cause traffic accidents. This finding suggests that steering wheel before the RtI and prepared for the coming automated driving hinders the driver’s situation awareness and situation. The average time was 18.6, 6.8 and 5.0 s, from as a result may affect the ability to grasp self-location. the left of the graph. Some studies have revealed that a driver took preliminary action for an RtI if the circumstances in which 3.3 Elapsed time until holding the steering wheel the RtI was issued is predictable (Merat et al.,2014); the results Table I shows the classification of the number of participants of this study support this conclusion. Especially in Scenario A, who held the steering wheel in the AO condition before and which involves leaving the expressway, the tendency to grasp the after the RtI. Note that data set of one participant in Scenarios steering wheel earlier than in other scenarios was confirmed. On A and B is missing. the other hand, even if the driver held the steering wheel before Figure 9 shows the time from issuing the RtI to taking over the RtI, a case of making a mistake in branching was confirmed. the wheel in the AO (0 s = RtI issue point). Error bars indicate This also occurred in our previous experiments. Therefore, the standard deviation. As a result of a one-factor ANOVA with when suddenly assuming control from automatic operation, the three levels of Scenarios A, B and C, the main effect was not driver may not distinguish the course that he/she should go to; significant [F(2, 21) = 0.99, p > 0.05]. However, as seen in as a result, there is a risk of creating a dangerous situation. Figure 8 Proportion that proceeded to the instructed direction 3.4 Point where lane change began In this experiment, although participants were not explicitly instructed to do so, almost all drivers used the turn signal when they began to change lanes. Thus, the point where the turn signal was made was analyzed as the starting point of lane changing. Six out of eight participants in the MO and five out of nine participants in the AO condition succeeded in branching in Scenario B. Even if the driver did not take control, the automated driving system was disengaged at the beginning of the zebra zone. The zebra zone was approximately 300 m. It was also possible to change lanes within the area: however, it is highly recommended to change lanes before entering the zebra zone. Figure 10 shows the point where the driver who successfully Table I Breakdown of the number of drivers classified by timing of holding changed lanes began to change lanes in Scenario B (AO: six the steering wheel in the AO participants, MO: five participants). The result of F-test showed Nine Participants Scenario (A) Scenario (B) Scenario (C) that the two populations are not equally distributed [F(4,5) = 84.325, p < 0.001)]. The result of Welch’stest showedno Before RtI 44 4 significant difference between the two groups [t(4.07) = 1.269, After RtI 44 5 p > 0.05]. However, most participants in MO seemed to start 103 Influence of automated driving Journal of Intelligent and Connected Vehicles Ryuichi Umeno, Makoto Itoh and Satoshi Kitazaki Volume 1 · Number 3 · 2018 · 99–106 There are also several limitations in this experiment. The Figure 10 Point where lane changing began in scenario B number of participants in this experiment is not sufficient to discuss the impact/effects of the results. Thus, it is important to consider which information can be beneficial or them in terms of designing an HMI for such circumstances. Moreover, even though the drivers were asked to watch TV during automated driving, most drivers were not always looking at the TV; they sometimes monitored their surroundings. This may be because one trial for each condition lasted at most approximately 20 min. If drivers are driving for a longer time, the driver may fall into the OoTL more. However, several failures at branch occurred even in the MO group. We need to keep in mind that there could be a participant who just cannot read the letters of the sign because of a problem with the resolution of the simulator. In this experiment, to alleviate this problem, the speed of the host vehicle was limited to 70 km/h, and the participants received supplementary explanation during instructions about the route (e.g. get off at the third exit to the left/go to the fourth branch to the right). lane-changing relatively earlier before the AO’s Rti point. In Some drivers who failed to branch remarked that they missed addition, even the two participants in the AO who successfully the branch point unintentionally. This could be due to their changed lanes performed lane change in the zebra zone. This is driving experience or highway experience. Thus, it is necessary not recommended for smooth traffic flow.Therefore,inScenario to analyze in detail whether the experience of manual driving B, where lane change to the right was required, it can be seen that will affect branch failures. the MO group prepared for the lane change ahead of the AO To design an HMI that allows drivers to take over safely and group. to recognize which direction they should go, it will be important to investigate whether the driver’s maneuvers and gaze will 4. Discussions change depending on the manner of presenting the course. The aim of this study was to investigate the influence of Larsson et al. (2015) suggested that a simpler HMI would be preferable for smooth RtI. automated driving on the driver’s own localization. The driving From the viewpoint of safety, if measures based on HMI simulator experiment involved two types of operation – manual alone are insufficient, then methods using tactile sense such as and autonomous operation – and all participants experienced directional and vibrotactile RtI (Fricke et al.,2015; Petermeijer three different road environments that simulated a Japanese et al.,2016; Petermeijer et al.,2017) or haptic shared control highway. By forcing participants to choose an instructed lane (Abbink et al.,2012) are also conceivable. These approaches before junctions or interchanges in all driving scenarios, we can convey information to the driver through tactile. It is investigated the impact on the ability of the driver to grasp own necessary to further investigate how to design an HMI for position. automated driving and the limitations of RtI. The statistical results suggest that the driver is more likely to It is also important to design a system that does not issue the rely on the automated driving system. Although there is no RtI at the scene where the driver is forced to change lanes significant difference between the two groups, it seems that suddenly. However, there are various situations when a system automated driving influenced the advancement/movement must suddenly issue an RtI (e.g. accident ahead, road toward the correct course and the position at which the lane construction, system failure). Even if driving authority is change was made. For instance, in Scenario B, lane changing suddenly transferred, an HMI design that allows safe handover was required after an RtI, but drivers were unable to quickly that/allows safe handover requires further investigation. resume control owing to the expectation, which caused approximately half of failures. Also, it was suggested that it may 5. Conclusions take longer time to execute lane change under such a situation. On the other hand, in the other two scenarios where lane Automated driving may lower a driver’s situation awareness; change was not required, there is no driver who failed to take therefore, the main objective of this study was to investigate the over. These results suggest that if the driver does not know why influence of automation on understanding of driver’s own the RtI was issued and how to take over, a failed takeover may localization. The driving simulator experiment involved two occur more frequently. In addition, despite holding the steering types of operation: manual and automated operations, and all wheel after an RtI, cases where the lane change was made participants experienced three different scenarios. All drivers incorrectly or where the lane change was made in the zebra were instructed how to go to the course in advance. The car was zone were confirmed. This result suggests that even if the driver not equipped with a navigation system; thus, the driver had to responds to an RtI, the driver may not always understand the remember the destination. situation. Further investigation in a more complex road Furthermore, the automated operation group was instructed environment will be necessary. It would be useful to explain a to watch an Internet TV program during automated driving, driver via HMI why an RtI was issued and what to do next. and to take over the operation if an RtI was issued. 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Journal of Intelligent and Connected Vehicles – Emerald Publishing
Published: Feb 8, 2019
Keywords: Automated vehicles; Autonomous driving; Advanced driver assistant systems; Driver behaviors and assistance; Human–machine interfaces; Request to intervene
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