A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sighencea, Bogdan Ilie; Stanciu, Rareș Ion; Căleanu, Cătălin Daniel

doi:10.3390/s21227543

Cited by 55 publications

(27 citation statements)

References 110 publications

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“…Different variants of deep learning have been used for the trajectory prediction process. The three most used architectures are: (i) recurrent neural networks, (ii) convolutional neural networks and (iii) generative adversarial networks [186].…”

Section: Trajectory and Trackingmentioning

confidence: 99%

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: Trajectory and Trackingmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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“…However, pedestrian trajectory prediction is a complex task because humans may change directions suddenly depending on objects, vehicles, human interaction, etc. [26]. In these cases, it is difficult to make accurate prediction based on the trajectory.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Pedestrian Crossing Behavior Based on Surveillance Video

Zhou

Ren

Zhang

et al. 2022

Sensors

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Prediction of pedestrian crossing behavior is an important issue faced by the realization of autonomous driving. The current research on pedestrian crossing behavior prediction is mainly based on vehicle camera. However, the sight line of vehicle camera may be blocked by other vehicles or the road environment, making it difficult to obtain key information in the scene. Pedestrian crossing behavior prediction based on surveillance video can be used in key road sections or accident-prone areas to provide supplementary information for vehicle decision-making, thereby reducing the risk of accidents. To this end, we propose a pedestrian crossing behavior prediction network for surveillance video. The network integrates pedestrian posture, local context and global context features through a new cross-stacked gated recurrence unit (GRU) structure to achieve accurate prediction of pedestrian crossing behavior. Applied onto the surveillance video dataset from the University of California, Berkeley to predict the pedestrian crossing behavior, our model achieves the best results regarding accuracy, F1 parameter, etc. In addition, we conducted experiments to study the effects of time to prediction and pedestrian speed on the prediction accuracy. This paper proves the feasibility of pedestrian crossing behavior prediction based on surveillance video. It provides a reference for the application of edge computing in the safety guarantee of automatic driving.

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“…Compared with these methods, our model leverages the ability of deep neural networks to handle high-dimensional and large data. More recently, deep learning has been exploited for trajectory prediction [54]. Recurrent neural networks (RNNs) [1,4,60] have been explored first due to their ability to learn from temporal data.…”

Section: Trajectory Analysis and Predictionmentioning

confidence: 99%

Human Trajectory Prediction via Neural Social Physics

Yue¹,

Manocha²,

Wang³

2022

Preprint

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Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. The former include rule-based, geometric or optimization-based models, and the latter are mainly comprised of deep learning approaches. In this paper, we propose a new method combining both methodologies based on a new Neural Differential Equation model. Our new model (Neural Social Physics or NSP) is a deep neural network within which we use an explicit physics model with learnable parameters. The explicit physics model serves as a strong inductive bias in modeling pedestrian behaviors, while the rest of the network provides a strong data-fitting capability in terms of system parameter estimation and dynamics stochasticity modeling. We compare NSP with 15 recent deep learning methods on 6 datasets and improve the state-of-the-art performance by 5.56%-70%. Besides, we show that NSP has better generalizability in predicting plausible trajectories in drastically different scenarios where the density is 2-5 times as high as the testing data. Finally, we show that the physics model in NSP can provide plausible explanations for pedestrian behaviors, as opposed to black-box deep learning. Code is available: https://github.com/realcrane/Human-Trajectory-Prediction-via-Neural-Social-Physics.

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A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Cited by 55 publications

References 110 publications

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Prediction of Pedestrian Crossing Behavior Based on Surveillance Video

Human Trajectory Prediction via Neural Social Physics

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