Driver Agent Serial Number
Brie Hoffler
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].
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].
The results of the subjective evaluation regarding acceptability and distraction revealed that elderly drivers rated feedback support highest, whereas non-elderly drivers rated driving support highest. The result implies that elderly drivers tend to desire driving evaluation and feedback because they are concerned about their driving behavior. In contrast, non-elderly drivers, who generally have confidence in their own driving ability, tend to accept a new service or technology for fun and safer driving.
Parker et al. [17] suggested that driving behavior is determined based on the driving situation and the driving model acquired from the experiences of the drivers themselves. Thus, drivers will revert to the same driving behavior if the driver model does not change. Analysis of the relationship between the biofunctions of drivers and the collision rate suggests that elderly drivers who have self-awareness of their driving ability tend to drive more safely than do drivers without such self-awareness [11]. To change a driver model to a safer one is to make drivers aware of their own driving behavior (i.e., self-awareness).
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.
system. The system consists of a smartphone (Android), a portable communication robot, and a cloud. As the hub of the system, we developed a smartphone application that receives data from a camera, Controller Area Network (CAN), and other devices via Wi-Fi or Bluetooth, and uploads these data automatically to the cloud service. Furthermore, this application connects to the robot and to a turntable for the robot, and it relays action commands from the cloud to the robot and the turntable. The system is being developed as a common service for communication robots that have the functions of speaking and motion. However, the size of the robot is an important consideration regarding its use in an actual car. Hence, we chose the RoBoHoN (SHARP Co., Ltd.) model as the robot for use in our system. On the cloud, there are map data that include details of intersections and the speed limit of each road, support models consisting of several rules for controlling the agent, and driving evaluation algorithms for each traffic scene.
In this study, we conducted preliminary experiments using an actual car to evaluate the acceptability of and distraction by our proposed agent. To this end, we analyzed both the subjective evaluation and the fixation points of the subjects during driving.
The experiments were performed after obtaining the approval of the Nagoya University Ethics Committee. In advance of the experiments, we held a meeting with the corresponding department in Aichi Prefecture, Aichi Prefectural Police Headquarters, and Chubu District Transport Bureau regarding our agent system and the use of a robot in an actual car. As a result, we obtained approval from each department for us to conduct our experiments on public roads.
The subjects drove a car for approximately five to eight minutes around a predefined experimental course on the campus of Nagoya University (Figure 3). The course included an intersection with a stop sign, and pedestrian and parked car avoidance scenes, although the locations and numbers of pedestrians and parked cars around the course were not controlled by the experimenter. However, sufficient numbers of both scenes were encountered during the experiments. Ten individuals (six males and four females) with an average age of 41.4 years participated in the experiments. As the preliminary experiments were the first trial of using an agent in an actual car environment, we opted to avoid using elderly subjects to reduce the risk of a traffic accident.
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.
By design, our agent system is controlled automatically based on GPS and map data. However, there are no map data available for the campus of Nagoya University. Therefore, in our experiments, the agent was controlled by Wizard of Oz (WoZ). To control the agent manually, we defined two rules regarding driving support. One concerned the priority of information. In a real and uncontrolled environment, several types of traffic scene often occur at the same time.
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.
All subjects participated in experiments under both experimental conditions. The procedure of the experiment was as follows. Each subject communicated with RoBoHoN for 15 minutes before driving the car in order to familiarize themselves with the robot. After that, the subject drove the car around the practice course until they became proficient in driving the PRIUS (Toyota Co., Ltd.). They then drove around the experimental course once without the agent and twice with the agent.
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