Toshihiro Hiraoka

Hailong Liu, Junya Wada, Toshihiro Hiraoka et al.

Drivers with peripheral visual field defects may fail to notice pedestrians in their peripheral visual field, leading to delayed hazard awareness and increased collision risk. This study explores hanger reflex cue (HRC) as a driving assistance method for drivers with peripheral visual field defects, in which mechanical pressure is applied to specific regions of the head to facilitate anticipatory orientation toward potentially risky pedestrians and support safer driving. In a driving simulator experiment with 15 participants, we compared driving behavior with and without HRC during pedestrian encounters under simulated peripheral visual field defect. The results showed that HRC significantly shifted drivers' modal head rotation angle toward the risky pedestrian and significantly increased gaze duration toward that pedestrian. Collision occurrence was lower in the w/ HRC condition than in the w/o HRC condition, although the direct effect of HRC on collision occurrence showed only a marginal trend. A piecewise structural equation modeling analysis further suggested that HRC may contribute to collision reduction through a sequential pathway from head rotation to gaze allocation and then to collision occurrence. These findings provide preliminary evidence that HRC can support anticipatory attention allocation toward peripheral hazards and may offer a promising driving assistance method for drivers with visual field impairment.

Hailong Liu, Masaki Kuge, Toshihiro Hiraoka et al.

Level 3 automated vehicles (AVs) issue a request to intervene (RtI) when the automated driving system approaches its system limitations. Although this takeover transition is safety-critical, it is usually invisible to surrounding manually driven vehicle (MV) drivers. This study proposes an external human-machine interface (eHMI) called eHMI C+O that externalizes the RtI-related takeover status of a Level~3 AV using cyan and orange light bars. A driving-simulator experiment with 40 participants examined whether the proposed eHMI supports surrounding MV drivers during AV takeover scenarios. The results showed that, compared with the ADS-status-only eHMI condition, which is similar to ``Automated Driving Marker Lights,'' and the no-eHMI condition, the proposed eHMI C+O significantly improved participants' understanding of the AV's driving intention, their prediction of its behavior, and their perceived sufficiency of the information presented by the AV. It also reduced hesitation, increased confidence, and promoted earlier and larger increases in time headway after the RtI was issued. In the AV accident scenario, eHMI C+O significantly reduced the odds of accident involvement for the following MV compared with the no-eHMI condition, corresponding to a 76.8% reduction in accident odds. Exploratory path analysis suggested that the safety benefit of the proposed eHMI C+O may be associated with improved situation awareness and earlier defensive driving responses. These findings indicate that externalizing RtI-related takeover status can help surrounding drivers better understand Level 3 AVs and respond more safely during safety-critical takeover transitions.

Ryuji Matsuo, Hailong Liu, Toshihiro Hiraoka et al.

Level 3 automated driving systems (ADS) have attracted significant attention and are being commercialized. A level 3 ADS prompts the driver to take control by issuing a request to intervene (RtI) when its operational design domains (ODD) are exceeded. However, complex traffic situations can cause drivers to perceive multiple potential triggers of RtI simultaneously, causing hesitation or confusion during take-over. Therefore, drivers need to clearly understand the ADS's system limitations to ensure safe take-over. This study proposes a voice-based educational human machine interface~(HMI) for providing RtI trigger cues and reason to help drivers understand ADS's system limitations. The results of a between-group experiment using a driving simulator showed that incorporating effective trigger cues and reason into the RtI was related to improved driver comprehension of the ADS's system limitations. Moreover, most participants, instructed via the proposed method, could proactively take over control of the ADS in cases where RtI fails; meanwhile, their number of collisions was lower compared with the other RtI HMI conditions. Therefore, using the proposed method to continually enhance the driver's understanding of the system limitations of ADS through the proposed method is associated with safer and more effective real-time interactions with ADS.

Yuri Klebanov, Romi Mikulinsky, Tom Reznikov et al.

Trust is a multilayered concept with critical relevance when it comes to introducing new technologies. Understanding how humans will interact with complex vehicle systems and preparing for the functional, societal and psychological aspects of autonomous vehicles' entry into our cities is a pressing concern. Design tools can help calibrate the adequate and affordable level of trust needed for a safe and positive experience. This study focuses on passenger interactions capable of enhancing the system trustworthiness and data accuracy in future shared public transportation.

Hailong Liu, Toshihiro Hiraoka, Seiya Tanaka

As various driving automation system (DAS) are commonly used in the vehicle, the over-trust in the DAS may put the driver in the risk. In order to prevent the over-trust while driving, the trust state of the driver should be recognized. However, description variables of the trust state are not distinct. This paper assumed that the outward expressions of a driver can represent the trust state of him/her-self. The explicit behaviors when driving with DAS is seen as those outward expressions. In the experiment, a driving simulator with a driver monitoring system was used for simulating a vehicle with the adaptive cruise control (ACC) and observing the motion information of the driver. Results show that if the driver completely trusted in the ACC, then 1) the participants were likely to put their feet far away from the pedals; 2) the operational intervention of the driver will delay in dangerous situations. In the future, a machine learning model will be tried to predict the trust state by using the motion information of the driver.

Hailong Liu, Toshihiro Hiraoka, Takatsugu Hirayama et al.

The head-up display (HUD) is an emerging device which can project information on a transparent screen. The HUD has been used in airplanes and vehicles, and it is usually placed in front of the operator's view. In the case of the vehicle, the driver can see not only various information on the HUD but also the backgrounds (driving environment) through the HUD. However, the projected information on the HUD may interfere with the colors in the background because the HUD is transparent. For example, a red message on the HUD will be less noticeable when there is an overlap between it and the red brake light from the front vehicle. As the first step to solve this issue, how to evaluate the mutual interference between the information on the HUD and backgrounds is important. Therefore, this paper proposes a method to evaluate the mutual interference based on saliency. It can be evaluated by comparing the HUD part cut from a saliency map of a measured image with the HUD image.

Hailong Liu, Toshihiro Hiraoka

Levels one to three of driving automation systems~(DAS) are spreading fast. However, as the DAS functions become more and more sophisticated, not only the driver's driving skills will reduce, but also the problem of over-trust will become serious. If a driver has over-trust in the DAS, he/she will become not aware of hazards in time. To prevent the driver's over-trust in the DAS, this paper discusses the followings: 1) the definition of over-trust in the DAS, 2) a hypothesis of occurrence condition and occurrence process of over-trust in the DAS, and 3) a driving behavior model based on the trust in the DAS, the risk homeostasis theory, and the over-trust prevention human-machine interface.