Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment

Bazilinskyy, Pavlo; Dodou, Dimitra; Winter, Joost de

doi:10.1007/978-3-030-80012-3_18

Cited by 4 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 54 ] Egocentric information transmitted by the eHMI, like “OK TO CROSS”, compelled pedestrians more than allocentric information like “STOPPING”. [ 55 ] Moreover, the reliability of eHMIs was questioned as it might be affected by weather, [ 56 ] light condition, [ 29 ] and vehicle behaviour. [ 35 ] For example, in poor weather, pedestrians could not always read vehicle signs.…”

Section: Human Behaviour Studies On Interactionsmentioning

confidence: 99%

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Crosato,

Tian,

Shum

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

show abstract

Section: Human Behaviour Studies On Interactionsmentioning

confidence: 99%

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Crosato,

Tian,

Shum

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…The implementation of AVs and the associated investigation of the interaction between AVs and other road users enlarge the application of multi-agent simulation. Bazilinskyy, Kooijman, Dodou, and de Winter [26] analyzed the interaction between an AV communicating via an eHMI, a human driver, and a pedestrian at a T-intersection with a zebra-crossing. The authors concluded that the multi-agent simulation is a promising tool to research interaction in traffic in the future.…”

Section: Previous Studies On Multi-agent Simulationmentioning

confidence: 99%

Multi-Vehicle Simulation in Urban Automated Driving: Technical Implementation and Added Benefit

et al. 2020

View full text Add to dashboard Cite

This article investigates the simultaneous interaction between an automated vehicle (AV) and its passenger, and between the same AV and a human driver of another vehicle. For this purpose, we have implemented a multi-vehicle simulation consisting of two driving simulators, one for the AV and one for the manual vehicle. The considered scenario is a road bottleneck with a double-parked vehicle either on one side of the road or on both sides of the road where an AV and a simultaneously oncoming human driver negotiate the right of way. The AV communicates to its passenger via the internal automation human–machine interface (HMI) and it concurrently displays the right of way to the human driver via an external HMI. In addition to the regular encounters, this paper analyzes the effect of an automation failure, where the AV first communicates to yield the right of way and then changes its strategy and passes through the bottleneck first despite oncoming traffic. The research questions the study aims to answer are what methods should be used for the implementation of multi-vehicle simulations with one AV, and if there is an added benefit of this multi-vehicle simulation compared to single-driver simulator studies. The results show an acceptable synchronicity for using traffic lights as basic synchronization and a distance control as the detail synchronization method. The participants had similar passing times in the multi-vehicle simulation compared to a previously conducted single-driver simulation. Moreover, there was a lower crash rate in the multi-vehicle simulation during the automation failure. Concluding the results, the proposed method seems to be an appropriate solution to implement multi-vehicle simulation with one AV. Additionally, multi-vehicle simulation offers a benefit if more than one human affects the interaction within a scenario.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Visual Attention of Pedestrians in Traffic Scenes: A Crowdsourcing Experiment

Cited by 4 publications

References 18 publications

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Multi-Vehicle Simulation in Urban Automated Driving: Technical Implementation and Added Benefit

Context-empowered Visual Attention Prediction in Pedestrian Scenarios

Contact Info

Product

Resources

About