Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Prédhumeau, Manon; Mancheva, Lyuba; Dugdale, Julie; Spalanzani, Anne

doi:10.1613/jair.1.13425

Cited by 16 publications

(36 citation statements)

References 57 publications

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“…In a couple of expert-based models (e.g., SFM), the parameters of the model are estimated by using real-world trajectory data [17], [19], [23], [68], [69]. In these methods, parameters such as safety distances, range of repulsive forces, payoff matrices of the game [19] or the social factor and perception zones in the SFM [68] are calibrated through a process to get the minimum difference between the ground-truth trajectories in the dataset and the simulated ones using the model.…”

Section: Combination Of Data-driven and Expert-based Modelsmentioning

confidence: 99%

“…From a broad perspective, these interaction effects can be categorized to be modelled either explicitly or implicitly. In the explicit interaction modelling approaches (e.g., see [17], [18], [70], [71]), the effect of the vehicle on a pedestrian's motion is forced through some clear terms in the formulation of the pedestrian's motion such as through explicit forces in the social force model (e.g., [68], [72]). On the other hand, datadriven modules often account for these interactions implicitly by using the vehicle's trajectory as another input to the model along with the target pedestrian's own trajectory (e.g., see [30], [45]).…”

Section: Rq2: Interaction Modelingmentioning

confidence: 99%

“…Some papers have modelled the vehicle with a circular shape same as how pedestrians are modelled in SFM but with a larger radius [68], [77]. In these models, the asymmetrical effect of the vehicle's influence is accounted for in the anisotropy function or considering the approach angle and its change rate [68], [77].…”

Section: A Explicit Interaction Modelingmentioning

confidence: 99%

See 2 more Smart Citations

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: Combination Of Data-driven and Expert-based Modelsmentioning

confidence: 99%

Section: Rq2: Interaction Modelingmentioning

confidence: 99%

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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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“…On the other hand, glass box models offer the advantage of interpretability and transparency by providing explanations for the mechanisms in relatively great detail. These models rely on different modelling paradigms including agent-based modelling (Bonabeau, 2002;Prédhumeau et al, 2022), optimal control theory (Le & Malikopoulos, 2022;Ross, 2015), Markovian processes (Bellman, 1957;Hsu et al, 2018), evidence accumulation (Pekkanen et al, 2022;Ratcliff et al, 2016), proxemics (Domeyer et al, 2019), discrete choice modelling (Hensher & Johnson, 2018;Zhao et al, 2019) and game theory (Elvik, 2014).…”

Section: Introductionmentioning

confidence: 99%

Driver-pedestrian interactions at unsignalised crossings are not in line with the Nash equilibrium

Kalantari¹,

Yang²,

Merat³

et al. 2023

Preprint

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Modelling human-robot interaction in the road traffic context is an evolving yet understudied area. Recent developments in vehicle automation require simulations of such interactions, which can be achieved by computational models of human behaviour such as game theory. Game theory is a modelling paradigm that provides a good insight into road user behaviour by considering agents’ interdependencies. However, it is still unclear whether conventional game theory is suitable for modelling vehicle-pedestrian interactions at unsignalised locations or if more complex models like behavioural game theory are needed. Hence, we compared four game-theoretic models based on two different payoff formulations and two solving algorithms obtained from the conventional and behavioural game theory literature, to answer this question. Data from 32 pedestrian-driver pairs, who interacted with each other in a distributed simulator study was used to test and validate the models. The study was conducted by connecting a CAVE-based pedestrian simulator to a motion-based driving simulator providing a safe and controlled environment to replicate the traffic scenarios. The findings demonstrated that there is a high variability between participant pairs’ behaviours. Our proposed behavioural game-theoretic model outperformed other models in predicting the interaction outcome as a function of kinematics and crossing type. The model can also predict which interaction will take the longest time to resolve, something that traditional models such as logit and conventional game theory are incapable of. According to our results, road users cannot be expected to behave in line with the Nash equilibrium of conventional game theory which underscores the complexity of human behaviour with implications for the testing and development of automated vehicles.

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“…The deployment of autonomous vehicles (AVs) is still facing many challenges, particularly their application in complex interactive environments, where different types of road users are included, e.g., pedestrians [ 1 , 2 , 3 ]. In such interactive environments, AVs not only need to make appropriate motion decisions but also need to consider other road users’ responses to AVs [ 4 ]. However, currently, AVs generally consider pedestrians as dynamic obstacles without intention and generally make simple braking decisions and actions to stop when AVs encounter pedestrians, especially in the scenarios of pedestrians crossing the street [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study

Sun

Zhang

et al. 2022

Sensors

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One of the major challenges for autonomous vehicles (AVs) is how to drive in shared pedestrian environments. AVs cannot make their decisions and behaviour human-like or natural when they encounter pedestrians with different crossing intentions. The main reasons for this are the lack of natural driving data and the unclear rationale of the human-driven vehicle and pedestrian interaction. This paper aims to understand the underlying behaviour mechanisms using data of pedestrian–vehicle interactions from a naturalistic driving study (NDS). A naturalistic driving test platform was established to collect motion data of human-driven vehicles and pedestrians. A manual pedestrian intention judgment system was first developed to judge the pedestrian crossing intention at every moment in the interaction process. A total of 98 single pedestrian crossing events of interest were screened from 1274 pedestrian–vehicle interaction events under naturalistic driving conditions. Several performance metrics with quantitative data, including TTC, subjective judgment on pedestrian crossing intention (SJPCI), pedestrian position and crossing direction, and vehicle speed and deceleration were analyzed and applied to evaluate human-driven vehicles’ yielding behaviour towards pedestrians. The results show how vehicles avoid pedestrians in different interaction scenarios, which are classified based on vehicle deceleration. The behaviour and intention results are needed by future AVs, to enable AVs to avoid pedestrians more naturally, safely, and smoothly.

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Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Cited by 16 publications

References 57 publications

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

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

Driver-pedestrian interactions at unsignalised crossings are not in line with the Nash equilibrium

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study

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