Pedestrian Behavior in Shared Spaces With Autonomous Vehicles: An Integrated Framework and Review

Prédhumeau, Manon; Spalanzani, Anne; Dugdale, Julie

doi:10.1109/tiv.2021.3116436

Cited by 27 publications

(15 citation statements)

References 137 publications

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“…One example of environments with alleviated structure and traffic rules is the shared spaces which are urban environments with removed curbs, road surface markings, and traffic signs. Different traffic agents in these environments are encouraged to share the space and negotiate the priority based on social rules [13]. Therefore, diverse interaction scenarios with different approach directions similar to off-road interaction Fig.…”

Section: Review Methodsmentioning

confidence: 99%

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Golchoubian

Ghafurian

Dautenhahn

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle's interactions with the pedestrians on pedestrians' future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed.

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Section: Review Methodsmentioning

confidence: 99%

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Golchoubian

Ghafurian

Dautenhahn

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…Those studies often assume that pedestrians observe the vehicle's behaviour and use this to infer the intentions of the approaching driver/AV. However, direct research on any such pedestrian behaviour estimation process is very limited (Predhumeau et al, 2021;Rasouli and Tsotsos, 2019). Sun et al (2015) (2022) provides some model-based evidence in support of a separate process of behaviour estimation by pedestrians but does not collect any subjective estimation data.…”

Section: Research Gaps and Questionsmentioning

confidence: 99%

Braking behaviour of automated vehicles as critical implicit communication for pedestrians’ vehicle behaviour estimation and road crossing

Tian¹,

Tzigieras²,

Wei³

et al. 2023

Preprint

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The deployment of automated vehicles (AVs) has generated high societal expectations. However, due to the absence of the driver role, the communication issues between pedestrians and AVs have not yet been solved. Previous research has shown the crucial role of implicit signals in this context. However, it is still unclear how pedestrians subjectively estimate vehicle behaviour and if pedestrians incorporate these estimations as part of their crossing decisions. For the first time, this study explored the impact of implicit signals both on pedestrians' subjective estimations of approaching vehicle behaviour across a wide range of experimental traffic scenarios and on their crossing behaviour in the same scenarios through a comprehensive analysis. Two simulator tasks, i.e., a natural road crossing task and a vehicle behaviour estimation task, were designed with time to collision, vehicle speed, and braking behaviour controlled. A novel finding is that the correlation between crossing decisions and vehicle behaviour estimations depends on the traffic scenario. That is, pedestrians could recognise the vehicle's different braking behaviours and that their crossing decisions aligned well with their subjective estimates, supporting the idea that pedestrians actively estimate vehicle behaviour as part of their decision-making process. However, if the traffic gap is big enough, there is a separation between crossing decisions and estimations about vehicle behaviour. We further showed that pedestrians crossed the street earlier and estimated yielding behaviour more accurately in early-onset braking scenarios than in late-onset braking scenarios. Interestingly, vehicle speed significantly affected pedestrian behaviour and estimations, with pedestrians tending to perceive low vehicle speed as yielding behaviour regardless of whether the vehicle yielded. Finally, we showed that visual cue $\dot{\tau}$ is associated with detecting vehicle-yielding behaviour. Our findings reveal in detail the impacts of implicit signals on pedestrian crossing decisions and have implications for road crossing safety and the development of AVs.

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“…Current mobile robots demonstrate the capability to navigate safely within their operational environments, effectively avoiding both static and dynamic obstacles [5]. However, from the human perspective, there is a preference for maintaining a certain distance from robots to ensure safety and comfort within the environment [6, 7]. Thus, treating humans merely as dynamic obstacles to be avoided is insufficient.…”

Section: Introductionmentioning

confidence: 99%

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

Li,

Zhang,

2024

Robotica

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In order to ensure safe and comfortable human–robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm. The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a “first-come, first-served” expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system. The results clearly demonstrate that the HAN system outperforms the traditional Dynamic Window Approach algorithm in ensuring the safety and comfort of humans in human–robot coexistence environments.

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Pedestrian Behavior in Shared Spaces With Autonomous Vehicles: An Integrated Framework and Review

Cited by 27 publications

References 137 publications

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Braking behaviour of automated vehicles as critical implicit communication for pedestrians’ vehicle behaviour estimation and road crossing

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

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