How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study

Winter, Joost de; Bazilinskyy, Pavlo; Wesdorp, Dale; Vlam, Valerie de; Hopmans, Belle; Visscher, Just; Dodou, Dimitra

doi:10.1080/00140139.2020.1862310

Cited by 24 publications

(9 citation statements)

References 40 publications

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“…While vehicle kinematics combined with such an AR concept may already offer a rich cue, there is still the issue that a red car may be perceived as yielding (rather than the green car) [2,30]. Therefore, redundancy gain was employed [65] by superimposing standard icons representing yielding (walking human figure) and non-yielding (hand palm) on the windshield, considering that pedestrians are likely to focus their visual attention on that region [9,66].…”

Section: Concept 2: Planes On Vehiclementioning

confidence: 99%

Towards future pedestrian-vehicle interactions: Introducing theoretically-supported AR prototypes

Tabone

Lee

Merat

et al. 2021

13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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The future urban environment may consist of mixed traffic in which pedestrians interact with automated vehicles (AVs). However, it is still unclear how AVs should communicate their intentions to pedestrians. Augmented reality (AR) technology could transform the future of interactions between pedestrians and AVs by offering targeted and individualized communication. This paper presents nine prototypes of AR concepts for pedestrian-AV interaction that are implemented and demonstrated in a real crossing environment. Each concept was based on expert perspectives and designed using theoretically-informed brainstorming sessions. Prototypes were implemented in Unity MARS and subsequently tested on an unmarked road using a standalone iPad Pro with LiDAR functionality. Despite the limitations of the technology, this paper offers an indication of how future AR systems may support future pedestrian-AV interactions. CCS CONCEPTS• Human-centered computing; • Human computer interaction (HCI); • Interaction paradigms; • Mixed / augmented reality;

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Section: Concept 2: Planes On Vehiclementioning

confidence: 99%

Towards future pedestrian-vehicle interactions: Introducing theoretically-supported AR prototypes

Tabone

Lee

Merat

et al. 2021

13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Self Cite

View full text Add to dashboard Cite

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“…It will not be long, however, before drivers of autonomous vehicles (AVs) will be unable to communicate their intention, as they will be engaged in activities unrelated to driving, such as reading, typing, or watching a movie [5,6]. This situation may prove difficult for pedestrians especially, because when deciding whether to cross the street or not, they take into consideration information provided by both vehicle kinematics, such as speed and acceleration [7][8][9], and the driver of an approaching vehicle, such as gaze direction and facial expression, according to a plethora of research conducted in different cultural contexts, such as France, China, the Czech Republic, Greece, the Netherlands, and the UK [4,[10][11][12][13][14][15][16][17][18][19]. To make matters worse, there is ample evidence for the underestimation of vehicle speed on the part of pedestrians, as well as the overestimation of the time at their disposal to attempt crossing the street safely [20][21][22].…”

Section: Introduction 1interaction In Trafficmentioning

confidence: 99%

Smiles and Angry Faces vs. Nods and Head Shakes: Facial Expressions at the Service of Autonomous Vehicles

Rouchitsas

Alm

2023

MTI

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When deciding whether to cross the street or not, pedestrians take into consideration information provided by both vehicle kinematics and the driver of an approaching vehicle. It will not be long, however, before drivers of autonomous vehicles (AVs) will be unable to communicate their intention to pedestrians, as they will be engaged in activities unrelated to driving. External human–machine interfaces (eHMIs) have been developed to fill the communication gap that will result by offering information to pedestrians about the situational awareness and intention of an AV. Several anthropomorphic eHMI concepts have employed facial expressions to communicate vehicle intention. The aim of the present study was to evaluate the efficiency of emotional (smile; angry expression) and conversational (nod; head shake) facial expressions in communicating vehicle intention (yielding; non-yielding). Participants completed a crossing intention task where they were tasked with deciding appropriately whether to cross the street or not. Emotional expressions communicated vehicle intention more efficiently than conversational expressions, as evidenced by the lower latency in the emotional expression condition compared to the conversational expression condition. The implications of our findings for the development of anthropomorphic eHMIs that employ facial expressions to communicate vehicle intention are discussed.

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“…The visual behavior of pedestrians in a street crossing situation is influenced by the concrete layout of the street [13,41], but also to a large extent by the existence of the time pressure [48,2]. Due to its importance to traffic safety, pedestrian attention has been studied extensively in human science [2,14,20,61]. Automatic prediction of pedestrian attention can open up the possibility to create more realistic training environments both for humans and Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Context-empowered Visual Attention Prediction in Pedestrian Scenarios

Vozniak¹,

Mueller²,

Hell³

et al. 2022

Preprint

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Effective and flexible allocation of visual attention is key for pedestrians who have to navigate to a desired goal under different conditions of urgency and safety preferences. While automatic modelling of pedestrian attention holds great promise to improve simulations of pedestrian behavior, current saliency prediction approaches mostly focus on generic free-viewing scenarios and do not reflect the specific challenges present in pedestrian attention prediction. In this paper, we present Context-SalNET, a novel encoder-decoder architecture that explicitly addresses three key challenges of visual attention prediction in pedestrians: First, Context-SalNET explicitly models the context factors urgency and safety preference in the latent space of the encoder-decoder model. Second, we propose the exponentially weighted mean squared error loss (ew-MSE) that is able to better cope with the fact that only a small part of the ground truth saliency maps consist of non-zero entries. Third, we explicitly model epistemic uncertainty to account for the fact that training data for pedestrian attention prediction is limited. To evaluate Context-SalNET, we recorded the first dataset of pedestrian visual attention in VR that includes explicit variation of the context factors urgency and safety preference. Context-SalNET achieves clear improvements over state-of-the-art saliency prediction approaches as well as over ablations. Our novel dataset will be made fully available and can serve as a valuable resource for further research on pedestrian attention prediction.

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How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study

Cited by 24 publications

References 40 publications

Towards future pedestrian-vehicle interactions: Introducing theoretically-supported AR prototypes

Towards future pedestrian-vehicle interactions: Introducing theoretically-supported AR prototypes

Smiles and Angry Faces vs. Nods and Head Shakes: Facial Expressions at the Service of Autonomous Vehicles

Context-empowered Visual Attention Prediction in Pedestrian Scenarios

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