Sav2p

Böckle, Marc-Philipp; Brenden, Anna Pernestål; Klingegård, Maria; Habibovic, Azra; Bout, Martijn

doi:10.1145/3131726.3131765

Cited by 76 publications

(25 citation statements)

References 6 publications

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“…Together, these studies indicate that pedestrians are likely to experience a feeling of stress and unsafety when encountering an AV, implying that current interaction patterns and strategies with conventional vehicles cannot be directly transferred to AVs. Similar findings were reported in our previous study (Malmsten Lundgren et al, 2017) as well as by Merat et al (2016) and Böckle et al (2017).…”

Section: Discussionsupporting

confidence: 93%

“…They implied that knowing the mode of the vehicle would allow them early to distinguish what type of vehicle they are encountering, and enable them to align their expectations. Similarly, knowing the intentions of the vehicle would eliminate possible ambiguities due to the lack of communication with the “driver.” This is in line with the conclusions presented by Merat et al (2016) and Böckle et al (2017) where interactions between fully automated shuttles and pedestrians have been investigated in real-world traffic and using virtual reality, respectively.…”

Section: Introductionsupporting

confidence: 92%

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Communicating Intent of Automated Vehicles to Pedestrians

et al. 2018

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While traffic signals, signs, and road markings provide explicit guidelines for those operating in and around the roadways, some decisions, such as determinations of “who will go first,” are made by implicit negotiations between road users. In such situations, pedestrians are today often dependent on cues in drivers’ behavior such as eye contact, postures, and gestures. With the introduction of more automated functions and the transfer of control from the driver to the vehicle, pedestrians cannot rely on such non-verbal cues anymore. To study how the interaction between pedestrians and automated vehicles (AVs) might look like in the future, and how this might be affected if AVs were to communicate their intent to pedestrians, we designed an external vehicle interface called automated vehicle interaction principle (AVIP) that communicates vehicles’ mode and intent to pedestrians. The interaction was explored in two experiments using a Wizard of Oz approach to simulate automated driving. The first experiment was carried out at a zebra crossing and involved nine pedestrians. While it focused mainly on assessing the usability of the interface, it also revealed initial indications related to pedestrians’ emotions and perceived safety when encountering an AV with/without the interface. The second experiment was carried out in a parking lot and involved 24 pedestrians, which enabled a more detailed assessment of pedestrians’ perceived safety when encountering an AV, both with and without the interface. For comparison purposes, these pedestrians also encountered a conventional vehicle. After a short training course, the interface was deemed easy for the pedestrians to interpret. The pedestrians stated that they felt significantly less safe when they encountered the AV without the interface, compared to the conventional vehicle and the AV with the interface. This suggests that the interface could contribute to a positive experience and improved perceived safety in pedestrian encounters with AVs – something that might be important for general acceptance of AVs. As such, this topic should be further investigated in future studies involving a larger sample and more dynamic conditions.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionsupporting

confidence: 92%

Communicating Intent of Automated Vehicles to Pedestrians

et al. 2018

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“…The application of eHMIs could help to increase the amount of safe street crossing by participants in front of a vehicle (Lagström & Lundgren, 2015). In addition, VRUs perceived comfort increased significantly during the interaction, when an eHMI was applied, in contrast to when no eHMI was used (Böckle et al, 2017). Other studies have investigated the effects of eHMIs on VRUs behavior (e.g., Chang, Toda, Sakamoto, & Igarashi, 2017; Rothenbücher et al, 2016).…”

Section: The Prospective Transformation Of Communication In Automatedmentioning

confidence: 97%

“…External human-machine interfaces (eHMIs) offer one opportunity for AVs to communicate with other traffic participants and thereby enhance traffic flow, road safety (Schieben et al, 2019) and feeling of safety of surrounding traffic participants (Böckle, Brenden, Klingegård, Habibovic, & Bout, 2017;de Clercq, Dietrich, Núñez Velasco, de Winter, & Happee, 2019). For instance, eHMIs might communicate with other traffic participants by providing a variety of information about the vehicles' states and future maneuvers (Schieben et al, 2019) as well as compensate for the lack of eye contact between the driver and other traffic participants (Lundgren et al, 2017).…”

Section: The Prospective Transformation Of Communication In Automatedmentioning

confidence: 99%

“…Previous studies have found a positive effect of eHMI application on emotional ratings (Lagström & Lundgren, 2015). Particularly, VRUs' feeling of safety significantly increased when interacting with a vehicle containing an eHMI, in contrast to when no eHMI was presented (Böckle et al, 2017;de Clercq et al, 2019). The application of eHMIs could help to increase the amount of safe street crossing by participants in front of a vehicle (Lagström & Lundgren, 2015).…”

Section: The Prospective Transformation Of Communication In Automatedmentioning

confidence: 99%

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Effects of a light-based communication approach as an external HMI for Automated Vehicles - a Wizard-of-Oz Study

Hensch¹,

Neumann²,

Beggiato³

et al. 2020

ToTS

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Communication between automated vehicles (AVs) and vulnerable road users (VRUs) is highly relevant in coordinating traffic maneuvers and therefore ensuring road safety. Especially in shared spaces such as parking areas, communication is highly important. As automated driving changes the driver's role, communication between different traffic participants will also change. External human-machine interfaces (eHMIs) may enhance safety and effective communication between VRUs and AVs by providing relevant information to other traffic participants if informal communication is insufficient. Hence, a variety of information (e.g., about AVs' driving mode or future maneuvers) is recommended to be communicated to VRUs. Therefore, we investigated the effects of three different light signals, presented by a light bar placed on the test vehicle's roof, as a form of an eHMI: automation mode, starting mode and crossing mode. Moreover, two different driving conditions (i.e., a manual and a simulated automated driving condition) were implemented to investigate the effects between these conditions. Either the driver was visible in the manual condition or the vehicle appeared driverless as a seat suit covered the driver in the simulated automated condition (Wizard-of-Oz design). A total of N = 173 random pedestrians passing by were interviewed and behavioral data were collected from over 1500 pedestrians. Results indicate that participants felt significantly safer during the interaction with the vehicle when a driver was visible. Although VRUs evaluated the general approach of applying light signals as eHMIs for AVs as useful, they assessed the presented light signals as only partially trustworthy and rather unintuitive. Moreover, many participants were unsure of whether the light signals were directed towards them, thus the directedness of light signals should be considered when implementing a light-based eHMI in AVs. Further, moving light signals attracted more attention (i.e., increased pedestrians' head movements towards the vehicle) than a steady or no light signal did. Interestingly, no difference existed between the investigated steady light signal and the baseline condition (i.e., no light signal) regarding head movements towards the vehicle. The results underline the importance of implementing an appropriate eHMI design in AVs.

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Turing in the driver's seat: Can people distinguish between automated and manually driven vehicles?

Stanton

Eriksson

Banks

et al. 2020

Hum Ftrs & Erg Mfg Svc

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As the number of automated vehicles is increasing on our roads, we wanted to know if people could detect if a car was being driven by a human driver or automation in a lane change task. This is particularly relevant, as most of the road collisions involve automated vehicles being struck from behind by manually driven vehicles. To address the detection of automated vehicles, an online survey presented videos of lane change maneuvers on multi-lane carriageways from behind the automated vehicle. We reasoned that, on such roads, the behavior of the vehicles in front would have more of an effect on drivers than those of the vehicles behind. To this end, an online survey was conducted with 769 people judging 60 video clips, classifying the lane change either being performed by Autopilot software or a human driver. Over 34,000 responses were recorded. It was found that automated and manual lane changes were virtually indistinguishable from the rear of the vehicle. The main conclusion of the research was that vehicles in automated mode should display this fact to other road users all around the vehicle as this may have an effect on other road users in anticipating the behavior of the other vehicle.

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Sav2p

Cited by 76 publications

References 6 publications

Communicating Intent of Automated Vehicles to Pedestrians

Communicating Intent of Automated Vehicles to Pedestrians

Effects of a light-based communication approach as an external HMI for Automated Vehicles - a Wizard-of-Oz Study

Turing in the driver's seat: Can people distinguish between automated and manually driven vehicles?

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