In this paper, we first introduce our previous experiments in Section 2, and system of proposed agent in Section 3. Then, in Section 4, we describe a set of preliminary experiments using our agent in an actual car environment designed to evaluate the level of its acceptability and distraction based on subjective evaluation and analysis of driver fixation points. Finally, we discuss the results derived from the experiments.
Three forms of driving support agent have been considered: a voice agent such as a car navigation system, a visual agent displayed on an LCD monitor around a dashboard or on a smartphone, and a robot set around a dashboard. A previous experiment was conducted where these three agent forms provided the same driving support to elderly and non-elderly drivers [13].
Driver Agent Serial Number.epub
The results suggested that the robot form was significantly more noticeable, familiar, and acceptable than the other two agent forms to both elderly and non-elderly drivers. In particular, the elderly found sudden vocal support difficult to understand, whereas the robot motion induced a sound that indicated when the agent was about to offer support. This feature could be seen as advanced notice of the offer of support via a mode other than vision, which could help drivers focus their attention on the offered support. For non-elderly drivers, coping with vocal support was not a difficulty; however, they found the visual agent too distracting, which led them to evaluate the form as least acceptable.
The robot form is a physical object and has stronger presence than the other forms. Analysis of driver fixation points during driving indicated that the presence of an agent does not necessarily lead to huge disturbance while driving. For the elderly, fixation points during driving diverged most with the voice agent and converged most with the robot agent. It has been reported that the accident rate could be reduced considerably if elderly drivers were accompanied by a fellow passenger, which has become known as the fellow passenger effect [14] [15]. The results revealed that the divergence of fixation points whilst driving was suppressed if the form of the agent was presented more clearly. This implies that the robot agent might trigger the fellow passenger effect because elderly drivers tend to consider a robot as a fellow passenger.
The proposed agent would be expected to improve driving behavior via two support functions: driving support and review support. Thus, a DS experiment was conducted in which elderly and non-elderly drivers were presented with three different supports: driving support only, review support only, and their combined use [12]. We analyzed the changes both in driving performance over three weeks and in subjective evaluation of the agent. Driving performance was evaluated using three indices: safe confirmation time at an intersection with a stop sign, and minimum passing speed and maximum width in pedestrian/parked car avoidance. For example, after three weeks use of combined support, the safe confirmation time of elderly drivers increased from 1.7 s to 3.6 s and that of non-elderly drivers increased from 1.9 s to 4.2 s. Moreover, the passing speed of elderly drivers reduced from 31.9 km/h to 16.1 km/h and that of non-elderly drivers reduced from 31.5 km/h to 18.9 km/h. The results for all three conditions revealed that use of an agent improved the driving behavior for both elderly and non-elderly drivers, and that the combined use of driving support and review support was most effective. Furthermore, analysis of the relationship between the biofunctions of elderly drivers and the improvement effect suggested that elderly drivers, whose cognitive or visual function were impaired because of aging, tend to take compensatory action based on the agent support [16].
In this study, with the aim of reducing the accident rate for elderly drivers, we proposed a driver agent system that provides driving support advice during driving and review support to encourage changes in driving behavior through self-awareness. Moreover, as an agent form, we selected a commercially available communication robot designed for home use, and we expected its acceptability to increase based on a sense of reliability and familiarity gained through daily usage.
In the experiments, we defined two experimental conditions: driving with the agent and driving without the agent. The robot agent was placed to the front and left of the driver (left side in Figure 2). Under the condition of driving with the agent, the agent provided driving support to the driver. In these experiments, the driving support comprised arousing driver attention. This support involved approach notifications regarding the intersection with a stop sign, pedestrians, and parked cars. On approaching each hazard, the agent provided support through vocalization and motion.
For example, a pedestrian and a parked car might be recognized at an intersection with a stop sign. In our experiments, the agent prioritized information regarding the intersection over that concerning pedestrians or parked cars. This was done because the location of the intersection was static and easily recognized on the map. In dealing with information on pedestrians and parked cars, priority was given to whichever was closer. The second rule concerned the reduction of frequency of information provision. Previous research has shown that support offered too frequently can annoy the driver. Therefore, if the same traffic situation was found to continue, the agent only provided information regarding the first one and it withheld other information for five seconds. For example, if there were three pedestrians in front of the car, the agent would provide an approach notification regarding the closest one but it would omit issuing notifications regarding the other pedestrians.
Logged data were collected from the CAN and the sensors of the smartphone (e.g., speed of car, acceleration, and GPS). Whilst driving, all subjects were equipped Tobii Pro Glasses 2 (Tobii Co., Ltd.) to collect data on driver fixation points. Moreover, when having completed the experiment, the subjects answered a questionnaire regarding the agent.
The aim of this study is to implement the driver agent in the real world to provide safe driving support services. Thus, we also conducted a questionnaire survey regarding the acceptable monthly cost of agent services. The average price suggested was US$13.5 per month. In addition, we conducted a Godspeed questionnaire [21] to evaluate the impression of the robot for considering whether there is the negative bias caused by the appearance or impression of the robot. The results of this questionnaire are shown in Figure 5. All the average scores were above score 3. In particular, likability was rated highest over score 4. Subjects reported positive impressions about the robot, which might have affected the results of other subjective evaluations. We also conducted a questionnaire to ask the subjects about the position of the agent in the car. We asked
If the existence of an agent attracts the attention of the driver whilst driving, the use of the agent could distract the driver, which might represent a problem concerning safety. Therefore, in our study, we analyzed driver fixation points and we calculated the proportion at which the subjects gazed at the agent whilst driving.
the two conditions would express the proportion to which the agent attracted the attention of the driver whilst driving. The recognition accuracy of fixation points is usually affected by differences both of individual subjects and of the environment. In this study, analysis was performed on fixation points collected from seven of the subjects.
The fixation points presented as a heat map recognized by the Tobii Pro Lab software under the conditions with and without an agent are shown in Figure 7. Areas that a driver gazed at frequently and for a long time are shown in red. It can be seen that the focus of attention of all drivers was usually the front space, although their gaze occasionally diverted to the rearview mirror or speedometer. During the experiments, each subject received driving support on 10 - 15 occasions. Only a few fixation points were observed around the position of the agent. The results of the distraction proportion under both conditions and the total duration that the subjects gazed at the AOI, calculated based on the number of fixation points, are shown in Figure 8. The accuracy of recognizing fixation points is different between subjects. Therefore, we normalized the distraction
The results of the subjective evaluation revealed that the acceptability of the agent was higher than score 4 (intermediate score), which is the same as in previous work [13]. In particular, although the subjects drove an actual car with an agent in these experiments, the agent was found not to annoy the subjects. There was almost no difference between the results obtained using an actual car and those derived from earlier DS experiments. Of the results derived in this study, only the reliability score of the Actual Car was lower than found following the DS experiments (p 2ff7e9595c
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