Early prediction of a pedestrian's trajectory at intersections

Goldhammer, Michael; Gerhard, Matthias; Zernetsch, Stefan; Doll, Konrad; Brunsmann, U.

doi:10.1109/itsc.2013.6728239

Cited by 32 publications

(18 citation statements)

References 10 publications

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“…Very few works report on the actual processing time of the algorithm on a given hardware, e.g. 2.5 fps in [143], some claim real-time performance [145]- [147], [151] whereas some indicate that the current implementation is not real-time [132], [142].…”

Section: B Understanding Pedestrians' Intentionsmentioning

confidence: 99%

Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice

Rasouli

Tsotsos

2020

IEEE Trans. Intell. Transport. Syst.

555

252

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One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between vehicles and pedestrians, the most vulnerable road users. Understanding pedestrian behavior, however, is not intuitive and depends on various factors, such as demographics of the pedestrians, traffic dynamics, environmental conditions, and so on. In this paper, we identify these factors by surveying pedestrian behavior studies, both the classical works on pedestrian-driver interaction and the modern ones that involve autonomous vehicles. To this end, we will discuss various methods of studying pedestrian behavior and analyze how the factors identified in the literature are interrelated. We will also review the practical applications aimed at solving the interaction problem, including design approaches for autonomous vehicles that communicate with pedestrians and visual perception and reasoning algorithms tailored to understanding pedestrian intention. Based on our findings, we will discuss the open problems and propose future research directions.

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Section: B Understanding Pedestrians' Intentionsmentioning

confidence: 99%

Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice

Rasouli

Tsotsos

2020

IEEE Trans. Intell. Transport. Syst.

555

252

View full text Add to dashboard Cite

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“…a) Single behavior: The probability of whether pedestrians intend to cross the roadway is computed in [8]- [12] using one or more of the following sources: motion information (previous path and current position), situation awareness (e.g., head pose), and contextual information (e.g., proximity to curb or intersection). Based on the predicted intention, the most likely behavior can be inferred, while other works directly compute a single trajectory [13], [14] or the time until the pedestrian will most likely cross [15]. b) Probability distribution: Predicting only a single behavior may suffice for short-term prediction; however, since many possible maneuvers exist, it is beneficial to compute a probability distribution of future behaviors by considering the possible goals of pedestrians [16]- [20].…”

Section: B Related Workmentioning

confidence: 99%

Set-Based Prediction of Pedestrians in Urban Environments Considering Formalized Traffic Rules

Koschi

Pek

Beikirch

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Set-based predictions can ensure the safety of planned motions, since they provide a bounded region which includes all possible future states of nondeterministic models of other traffic participants. However, while autonomous vehicles are tested in urban environments, a set-based prediction tailored to pedestrians does not exist yet. This paper addresses this problem and presents an approach for set-based predictions of pedestrians using reachability analysis. We obtain tight overapproximations of pedestrians' reachable occupancy by incorporating the dynamics of pedestrians, contextual information, and traffic rules. In addition, since pedestrians often disregard traffic rules, our constraints automatically adapt so that such behaviors are included in the prediction. Using datasets of recorded pedestrians, we validate our proposed method and demonstrate its use for evasive maneuver planning of automated vehicles.

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“…destination, age, physical conditions, emotion, unpredictable environment changes and so on. Practically, the pedestrian may change his/her speed and direction frequently and randomly, therefore, more complicated or stochastic models should be used to predict the pedestrian's trajectory [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Vision‐based approach for predicting the probability of vehicle–pedestrian collisions at intersections

Zhou

Peng

Cai

2020

IET Intelligent Trans Sys

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Road accidents impose serious problems on society. Possible collisions between vehicles and pedestrians must be detected before they occur so that a timely warning may be issued. By using the vision‐based approach, this study presents an effective and efficient algorithm to estimate the vehicle–pedestrian collision probability at intersections. The real‐time trajectories and movement parameters (position, speed, acceleration or direction) of vehicles and pedestrians are obtained based on state‐of‐the‐art detection and tracking algorithm which include background subtraction method, faster regions with convolutional neural networks and optical flow method. To find the appropriate time to identify the latent collision risk for calculating the collision probability, this study defines the critical time based on different collision patterns of perception‐reaction failure and evasive action failure. In addition, based on discrete acceleration and discrete angle, the authors get different extended trajectories which can include most situation when the conflict happened. Trajectories generation probability are given by the discrete choice probability model based on the Logit model to get the accurate collision probability. Real‐world video data is implemented to demonstrate the approach. This proposed collision prediction method can provide some important results for designing the intelligent pedestrian signal timing schemes at intersections.

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Early prediction of a pedestrian's trajectory at intersections

Cited by 32 publications

References 10 publications

Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice

Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice

Set-Based Prediction of Pedestrians in Urban Environments Considering Formalized Traffic Rules

Vision‐based approach for predicting the probability of vehicle–pedestrian collisions at intersections

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