Analysis on termination of pedestrians' gait at urban intersections

Goldhammer, Michael; Hubert, Andreas; Koehler, Sebastian; Zindler, Klaus; Brunsmann, U.; Doll, Konrad; Sick, Bernhard

doi:10.1109/itsc.2014.6957947

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…Generally, elderly pedestrians are physically less capable compared to adults, and as a result, they walk slower [9], have a more varied walking pattern (e.g. do not have steady velocity) [56] and are more cautious in terms of gap acceptance [40], [57].…”

Section: A Classical Studiesmentioning

confidence: 99%

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

Rasouli

Tsotsos

2020

IEEE Trans. Intell. Transport. Syst.

597

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: A Classical Studiesmentioning

confidence: 99%

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

Rasouli

Tsotsos

2020

IEEE Trans. Intell. Transport. Syst.

597

252

View full text Add to dashboard Cite

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“…Before the advent of deep learning techniques, typically pedestrian trajectories using Kalman Filters or naïve movement models using human gait estimation and analysis of simple heuristics [211,212]. However, due to the improbability of proper adaptation and handling to changes in pedestrian movement, these techniques provided poor results in terms of predicting future pedestrian movements [1,178].…”

Section: Cnn and Svmmentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.

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“…In general, the estimation of pedestrians' trajectories have traditionally been addressed using naive movement models based on human gait estimation and analysis of simple heuristics based on that information [14], [15]. Other traditional approaches have focused on the use of Kalman Filters (KF) to estimate pedestrian trajectories.…”

Section: B Related Workmentioning

confidence: 99%

“…Most of these existing techniques produced poor results due to the impossibility of properly handling and adapting to changes in pedestrians' movements [16]. More recently, a more complex method based on Artificial Neural Networks (ANN) has been proposed for pedestrian trajectory estimation and intention recognition [15]. This work is able to estimate pedestrian trajectory based on pedestrian head detection and the use of its position for tracking along the sequence.…”

Section: B Related Workmentioning

confidence: 99%

Pedestrian Movement Direction Recognition Using Convolutional Neural Networks

Dominguez-Sanchez

Cazorla

Orts-Escolano

2017

IEEE Trans. Intell. Transport. Syst.

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Pedestrian movement direction recognition is an important factor in autonomous driver assistance and security surveillance systems. Pedestrians are the most crucial and fragile moving objects in streets, roads and events where thousands of people may gather on a regular basis. People flow analysis on zebra crossings and in shopping centres or events such as demonstrations are a key element to improve safety and to enable autonomous cars to drive in real life environments. This paper focuses on deep learning techniques such as Convolutional Neural Networks (CNN) to achieve a reliable detection of pedestrians moving in a particular direction. We propose a CNN-based technique that leverages current pedestrian detection techniques (HOG-linSVM) to generate a sum of subtracted frames (flow estimation around the detected pedestrian), which are used as an input for the proposed modified versions of various state-ofthe-art CNN networks such as AlexNet, GoogleNet and ResNet. Moreover, we have also created a new dataset for this purpose, and analysed the importance of training in a known dataset for the neural networks to achieve reliable results.

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Analysis on termination of pedestrians' gait at urban intersections

Cited by 17 publications

References 19 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

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Pedestrian Movement Direction Recognition Using Convolutional Neural Networks

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