A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries

Jaipuria, Nikita; Habibi, Golnaz; How, Jonathan P.

doi:10.48550/arxiv.1806.09444

Cited by 5 publications

(5 citation statements)

References 12 publications

(14 reference statements)

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“…In the context of trajectory prediction, Scene-LSTM [8] partitions static scenes into Manhattan grids and utilizes LSTM to forecast pedestrian locations. CAR-Net [27] proposes an attention network that leverages scene semantic CNN to predict human trajectories. [25] introduces a binary two-dimensional occupancy grid, where static obstacles are represented by 1 and walkable areas by 0, effectively describing the scene's layout.…”

Section: Related Workmentioning

confidence: 99%

DMMNet: A Pedestrian Trajectory Prediction Method based on Decomposed Multimodal Modelling of Human Dynamics

Gao,

Miao,

Guoye

2024

Preprint

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Pedestrian trajectory prediction has wide applications in various engineering disciplines and in autonomous driving. It can effectively enhance traffic safety, improve traffic flow, optimize urban planning, and enhance the safety of autonomous driving. However, current methods can only perform unimodal predictions and cannot consider the diversity and uncertainty of pedestrian behavior. While some generative models can generate diverse prediction results, they cannot guarantee coverage of key modes and have limited control over the attributes of predicted trajectories. To address these issues, we propose a pedestrian trajectory prediction method based on Decomposed Multimodal Modeling of human dynamics, DMMNet, considering both the uncertainty of decision variables and the stochastic nature of random decision. Firstly, we adopt a decomposed modeling approach to effectively model the target uncertainty and the randomness of targets and paths in pedestrian trajectory prediction. This allows us to better capture the diversity and uncertainty of pedestrian behavior. Secondly, our method can generate explicit probability maps, providing better spatial constraints and control capabilities to improve the accuracy and interpretability of prediction results. This, in turn, offers better guidance and adaptability for other intelligent systems. Finally, our method extends the prediction range, capable of predicting pedestrian trajectories over a longer time period in the future. The proposed pedestrian trajectory prediction method in this paper has clear advantages in considering multimodality, providing spatial constraints, and expanding the application scope. It can offer more accurate, interpretable, and controllable pedestrian trajectory prediction capabilities for intelligent transportation systems and other related research and applications. The proposed method achieved an improvement of 47.7% in the ADE metric and 62.6% in the FDE metric on the ETH/UCY dataset. In the SDD dataset, there was an improvement of 18.4% in the ADE metric and 35.2% in the FDE metric.

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Section: Related Workmentioning

confidence: 99%

DMMNet: A Pedestrian Trajectory Prediction Method based on Decomposed Multimodal Modelling of Human Dynamics

Gao,

Miao,

Guoye

2024

Preprint

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“…Other work applies inverse reinforcement to perform the pedestrian trajectory prediction process [183]. Other works such as [165,176,184,185] integrate different factors such as speed, location, direction of the pedestrian's head and environmental into the process to predict intention and future trajectory.…”

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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“…For instance, [9] proposes Gaussian Process Dynamical Models based on the action of pedestrians and [13] uses an intent function with speed, location, and heading direction as input to predict future directions. Other works incorporate environment factors into trajectory prediction [10], [11], [14], [34]. Some other works observe the past trajectories and predict the future.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

Liu

Adeli

Cao

et al. 2020

IEEE Robot. Autom. Lett.

124

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Reasoning over visual data is a desirable capability for robotics and vision-based applications. Such reasoning enables forecasting the next events or actions in videos. In recent years, various models have been developed based on convolution operations for prediction or forecasting, but they lack the ability to reason over spatiotemporal data and infer the relationships of different objects in the scene. In this paper, we present a framework based on graph convolution to uncover the spatiotemporal relationships in the scene for reasoning about pedestrian intent. A scene graph is built on top of segmented object instances within and across video frames. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is very crucial piece of information for autonomous vehicles to navigate safely and more smoothly. We approach the problem of intent prediction from two different perspectives and anticipate the intention-to-cross within both pedestrian-centric and location-centric scenarios. In addition, we introduce a new dataset designed specifically for autonomousdriving scenarios in areas with dense pedestrian populations: the Stanford-TRI Intent Prediction (STIP) dataset. Our experiments on STIP and another benchmark dataset show that our graph modeling framework is able to predict the intention-to-cross of the pedestrians with an accuracy of 79.10% on STIP and 79.28% on Joint Attention for Autonomous Driving (JAAD) dataset up to one second earlier than when the actual crossing happens. These results outperform baseline and previous work. Please refer to http://stip.stanford.edu/ for the dataset and code.Index Terms-spatiotemporal graphs, forecasting, graph neural networks, autonomous-driving. Recent work [19]-[23] introduced pedestrian intent prediction and have typically tackled the problem by observing pedestrian-specific features such as location, velocity, and

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A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries

Cited by 5 publications

References 12 publications

DMMNet: A Pedestrian Trajectory Prediction Method based on Decomposed Multimodal Modelling of Human Dynamics

DMMNet: A Pedestrian Trajectory Prediction Method based on Decomposed Multimodal Modelling of Human Dynamics

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

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