Pedestrian intention prediction: A convolutional bottom-up multi-task approach

Razali, Haziq; Mordan, Taylor; Alahi, Alexandre

doi:10.1016/j.trc.2021.103259

Cited by 47 publications

(13 citation statements)

References 36 publications

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“…In [158], the authors mention that methods for predicting pedestrian intention fall into two groups: (i) methods that approach the problem as a trajectory prediction issue with the objective of creating a route and identifying whether that route will cross the street [159][160][161] and (ii) methods that solve it as a binary classification problem that results in the pedestrian intention [162][163][164][165].…”

Section: Intention Predictionmentioning

confidence: 99%

“…This work is improved in [169] by defining an attention mechanism that assigns a weight to each element participating within the driving environment based on its proximity. However, according to [158], this type of model suffers from several limitations such as the need for moving cameras to obtain a complete view of the scenario, which can cause errors in the accuracy of the trajectories. They do not make use of pedestrian pose information, which they say is a weakness because it is an important indicator of intent.…”

Section: Intention Predictionmentioning

confidence: 99%

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Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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Section: Intention Predictionmentioning

confidence: 99%

Section: Intention Predictionmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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“…Moreover, approximately 137,000 pedestrians were treated in emergency departments for nonfatal crash-related injuries in the United States in 2017. Currently, research on pedestrians focuses on the prediction of their trajectory [30], [31] or a system that is able to create a fine-grained map of hazard levels across a city, and a heuristic to identify interventions that might simultaneously improve pedestrian and vehicle safety. [29].…”

Section: Motivationmentioning

confidence: 99%

Generic Sensor Model Usecase Exemplified by Pedestrian Crossing

Lelowicz

Piłat

2022

IEEE Sensors J.

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This paper presents the application of a generic sensor model used in virtual validation of Advanced Driver-Assistance System. The aim is to demonstrate the feasibility and usefulness of such a model in a case study. This model transforms an incoming object list from virtual simulation to a sensor-specific object list and is applicable for all perception sensors acting at the object level. The procedure for parameterisation of the sensor model was explained and illustrated with examples of two algorithms: vision and lidar based. The validity of the model was confirmed using the case of a pedestrian crossing where incidents involving pedestrians occur. Based on simulation studies and the evaluation of the actual configuration of the pedestrian crossing, conclusions were drawn which may contribute to the increase of pedestrians safety, both on the passive and active side. The obtain results allow to improve the operation of the transportation system in the field of advanced driver assistance systems development.

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“…Human action recognition plays a vital role for distinguishing a particular behavior of interest in the video. It has critical applications including visual surveillance for detection of suspicious human activities to prevent the fatal accidents 1,2 , automation-based driving to sense and predict human behavior for safe navigation 3,4 . In addition, there are large amount of non-trivial applications such as human-machine interaction 5,6 , video retrieval 7 , crowd scene analysis 8 and identity recognition 9 .…”

Section: Introductionmentioning

confidence: 99%

Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer

Nguyen

Ribeiro

2023

Preprint

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Recognizing human actions in video sequences, known as Human Action Recognition (HAR), is a challenging task in pattern recognition. While Convolutional Neural Networks (ConvNets) have shown remarkable success in image recognition, they are not always directly applicable to HAR, as temporal features are critical for accurate classification. In this paper, we propose a novel dynamic PSO-ConvNet model for learning actions in videos, building on our recent work in image recognition. Our approach leverages a framework where the weight vector of each neural network represents the position of a particle in phase space, and particles share their current weight vectors and gradient estimates of the Loss function. To extend our approach to video, we integrate ConvNets with state-of-the-art temporal methods such as Transformer and Recurrent Neural Networks. Our experimental results on the UCF-101 dataset demonstrate substantial improvements of up to 9% in accuracy, which confirms the effectiveness of our proposed method. Overall, our dynamic PSO-ConvNet model provides a promising direction for improving HAR by better capturing the spatio-temporal dynamics of human actions in videos. The code is available at https://github.com/leonlha/Video-Action-Recognition-Collaborative-Learning-with-Dynamics-via-PSO-ConvNet-Transformer.

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Pedestrian intention prediction: A convolutional bottom-up multi-task approach

Cited by 47 publications

References 36 publications

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Generic Sensor Model Usecase Exemplified by Pedestrian Crossing

Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer

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