A. Rodríguez Palmeiro | TU Delft Repository

Interactions between cyclists and automated vehicles

Results of an international photo-based survey

Abstract (2018) - Ana Rodríguez Palmeiro, S. Van der Kint, Marjan Hagenzieker, I.N.L.G. van Schagen, Joost de Winter

Cyclists’ expectations when encountering automated vehicles

Results of an international photo-based questionnaire

Conference paper (2018) - A. Rodríguez Palmeiro, Sander van der Kint, M.P. Hagenzieker, J.C.F. de Winter

In the future, cyclists will be sharing the roads with automated and traditional vehicles [1]. Interactions between cyclists and automated vehicles (AVs) may differ from interactions with traditional vehicles, because cyclists may hold incorrect expectations about how AVs will react to their presence, leading to confusing and risky situations. The objective of this study was to assess the (self-reported) behaviour and expectations of cyclists when encountering an AV with different external features as compared to a traditional vehicle. This study builds on a smaller questionnaire conducted by Hagenzieker et al. [2]. 607 participants from 15 countries completed an online questionnaire, in which they were shown 12 photos from a cyclist's point of view. Each photo involved a traditional vehicle or an AV recognisable by a specific feature (door sign or roof sign with the message 'self-driving'). Moreover, three descriptions of the capabilities of AVs (negative, neutral, positive) were provided in a between-subjects design. Participants had to report, for each photo, (1) how sure they were that the car had noticed them, (2) how sure they were that the car would stop, both on a scale from unsure to sure, and (3) what they would do on a scale from 'increase speed' to 'wait'. Personal characteristics (trust in automation and sensation seeking) were also measured. The results showed that participants were more sure to be noticed by an AV with a door sign than by a traditional vehicle. They were also more sure that the car would stop when the vehicle was an AV. The type of AV description showed statistically significant interaction effects with vehicle type. Furthermore, results differed per country group. For example, European respondents were more sure to be noticed by the car than respondents from North America. Finally, trust and sensation seeking scores showed positive correlations with participants' answers to the three questions. ...

In the future, cyclists will be sharing the roads with automated and traditional vehicles [1]. Interactions between cyclists and automated vehicles (AVs) may differ from interactions with traditional vehicles, because cyclists may hold incorrect expectations about how AVs will react to their presence, leading to confusing and risky situations. The objective of this study was to assess the (self-reported) behaviour and expectations of cyclists when encountering an AV with different external features as compared to a traditional vehicle. This study builds on a smaller questionnaire conducted by Hagenzieker et al. [2]. 607 participants from 15 countries completed an online questionnaire, in which they were shown 12 photos from a cyclist's point of view. Each photo involved a traditional vehicle or an AV recognisable by a specific feature (door sign or roof sign with the message 'self-driving'). Moreover, three descriptions of the capabilities of AVs (negative, neutral, positive) were provided in a between-subjects design. Participants had to report, for each photo, (1) how sure they were that the car had noticed them, (2) how sure they were that the car would stop, both on a scale from unsure to sure, and (3) what they would do on a scale from 'increase speed' to 'wait'. Personal characteristics (trust in automation and sensation seeking) were also measured. The results showed that participants were more sure to be noticed by an AV with a door sign than by a traditional vehicle. They were also more sure that the car would stop when the vehicle was an AV. The type of AV description showed statistically significant interaction effects with vehicle type. Furthermore, results differed per country group. For example, European respondents were more sure to be noticed by the car than respondents from North America. Finally, trust and sensation seeking scores showed positive correlations with participants' answers to the three questions.

Interaction between pedestrians and automated vehicles

A Wizard of Oz experiment

Journal article (2018) - Ana Rodríguez Palmeiro, S. Van der Kint, Luuk Vissers, Haneen Farah, Joost C.F. de Winter, Marjan Hagenzieker

Automated vehicles (AVs) will be introduced on public roads in the future, meaning that traditional vehicles and AVs will be sharing the urban space. There is currently little knowledge about the interaction between pedestrians and AVs from the point of view of the pedestrian in a real-life environment. Pedestrians may not know with which type of vehicle they are interacting, potentially leading to stress and altered crossing decisions. For example, pedestrians may show elevated stress and conservative crossing behavior when the AV driver does not make eye contact and performs a non-driving task instead. It is also possible that pedestrians assume that an AV would always yield (leading to short critical gaps). This study aimed to determine pedestrians’ crossing decisions when interacting with an AV as compared to when interacting with a traditional vehicle. We performed a study on a closed road section where participants (N = 24) encountered a Wizard of Oz AV and a traditional vehicle in a within-subject design. In the Wizard of Oz setup, a fake ‘driver’ sat on the driver seat while the vehicle was driven by the passenger by means of a joystick. Twenty scenarios were studied regarding vehicle conditions (traditional vehicle, ‘driver’ reading a newspaper, inattentive driver in a vehicle with “self-driving” sign on the roof, inattentive driver in a vehicle with “self-driving” signs on the hood and door, attentive driver), vehicle behavior (stopping vs. not stopping), and approach direction (left vs. right). Participants experienced each scenario once, in a randomized order. This allowed assessing the behavior of participants when interacting with AVs for the first time (no previous training or experience). Post-experiment interviews showed that about half of the participants thought that the vehicle was (sometimes) driven automatically. Measurements of the participants’ critical gap (i.e., the gap below which the participant will not attempt to begin crossing the street) and self-reported level of stress showed no statistically significant differences between the vehicle conditions. However, results from a post-experiment questionnaire indicated that most participants did perceive differences in vehicle appearance, and reported to have been influenced by these features. Future research could adopt more fine-grained behavioral measures, such as eye tracking, to determine how pedestrians react to AVs. Furthermore, we recommend examining the effectiveness of dynamic AV-to-pedestrian communication, such as artificial lights and gestures. ...

Automated vehicles (AVs) will be introduced on public roads in the future, meaning that traditional vehicles and AVs will be sharing the urban space. There is currently little knowledge about the interaction between pedestrians and AVs from the point of view of the pedestrian in a real-life environment. Pedestrians may not know with which type of vehicle they are interacting, potentially leading to stress and altered crossing decisions. For example, pedestrians may show elevated stress and conservative crossing behavior when the AV driver does not make eye contact and performs a non-driving task instead. It is also possible that pedestrians assume that an AV would always yield (leading to short critical gaps). This study aimed to determine pedestrians’ crossing decisions when interacting with an AV as compared to when interacting with a traditional vehicle. We performed a study on a closed road section where participants (N = 24) encountered a Wizard of Oz AV and a traditional vehicle in a within-subject design. In the Wizard of Oz setup, a fake ‘driver’ sat on the driver seat while the vehicle was driven by the passenger by means of a joystick. Twenty scenarios were studied regarding vehicle conditions (traditional vehicle, ‘driver’ reading a newspaper, inattentive driver in a vehicle with “self-driving” sign on the roof, inattentive driver in a vehicle with “self-driving” signs on the hood and door, attentive driver), vehicle behavior (stopping vs. not stopping), and approach direction (left vs. right). Participants experienced each scenario once, in a randomized order. This allowed assessing the behavior of participants when interacting with AVs for the first time (no previous training or experience). Post-experiment interviews showed that about half of the participants thought that the vehicle was (sometimes) driven automatically. Measurements of the participants’ critical gap (i.e., the gap below which the participant will not attempt to begin crossing the street) and self-reported level of stress showed no statistically significant differences between the vehicle conditions. However, results from a post-experiment questionnaire indicated that most participants did perceive differences in vehicle appearance, and reported to have been influenced by these features. Future research could adopt more fine-grained behavioral measures, such as eye tracking, to determine how pedestrians react to AVs. Furthermore, we recommend examining the effectiveness of dynamic AV-to-pedestrian communication, such as artificial lights and gestures.