How Can I See My Future? FvTraj: Using First-Person View for Pedestrian Trajectory Prediction

Bi, Huikun; Zhang, Ruisi; Mao, Tianlu; Deng, Zhigang; Wang, Zhaoqi

doi:10.1007/978-3-030-58571-6_34

Cited by 15 publications

(3 citation statements)

References 40 publications

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“…For example, [20] extends the energy-based model for pedestrian trajectory prediction with a frustum of attention (30 • circular sector) towards where the pedestrian is heading. [21] combines top-down view and simulated first person view with limited vision and attention to predict pedestrian trajectories. Similarly, [22] uses pedestrian and vehicle perspectives to model their trajectories.…”

Section: Related Workmentioning

confidence: 99%

Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making

Kotseruba¹,

Rasouli²

2023

Preprint

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Current research on pedestrian behavior understanding focuses on the dynamics of pedestrians and makes strong assumptions about their perceptual abilities. For instance, it is often presumed that pedestrians have omnidirectional view of the scene around them. In practice, human visual system has a number of limitations, such as restricted field of view (FoV) and range of sensing, which consequently affect decision-making and overall behavior of the pedestrians. By including explicit modeling of pedestrian perception, we can better understand its effect on their decision-making. To this end, we propose an agent-based pedestrian behavior model Intend-Wait-Perceive-Cross with three novel elements: field of vision, working memory, and scanning strategy, all motivated by findings from behavioral literature. Through extensive experimentation we investigate the effects of perceptual limitations on safe crossing decisions and demonstrate how they contribute to detectable changes in pedestrian behaviors.

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Section: Related Workmentioning

confidence: 99%

Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making

Kotseruba¹,

Rasouli²

2023

Preprint

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“…Frossard et al [5] proposed the usage of a Convolutional-Recurrent architecture for detecting the turn signals and flashers in video sequences. Few [6,7] have also attempted to understand and predict the pedestrian intentions to improve the Driver Intention Recognition systems. Torstensson et al [8] proposed a Convolutional and LSTM based network to predict the actions of the in-vehicle driver.…”

Section: Driving Scene Understandingmentioning

confidence: 99%

Driving Scene Understanding: How much temporal context and spatial resolution is necessary?

Gaurav

Tripp

Narayan

2021

Proceedings of the Canadian Conference on Artificial Intelligence

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Driving Scene Understanding is a broad field which addresses the problem of recognizing a variety of on-road situations; namely driver behaviour/intention recognition, driver-action causal reasoning, pedestrians' and nearby vehicles' intention recognition, etc. Many existing works propose excellent AI based solutions to these interesting problems by leveraging visual data along with other modalities. However, very few researchers venture into determining the necessary metadata of the visual inputs to their models. This work attempts to put forward some useful insights about the required spatial resolution and temporal context/depth of the visual data for Driving Scene Understanding.

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“…12. FvTraj [45]: This model is based on a multi-head attention mechanism and uses a social perception attention module to simulate social interaction between pedestrians, as well as a view perception attention module to capture the relationship between historical motion states and visual features. 13.…”

mentioning

confidence: 99%

A Novel Trajectory Feature-Boosting Network for Trajectory Prediction

Peng

Zhu

et al. 2023

Entropy

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Trajectory prediction is an essential task in many applications, including autonomous driving, robotics, and surveillance systems. In this paper, we propose a novel trajectory prediction network, called TFBNet (trajectory feature-boosting network), that utilizes trajectory feature boosting to enhance prediction accuracy. TFBNet operates by mapping the original trajectory data to a high-dimensional space, analyzing the change rules of the trajectory in this space, and finally aggregating the trajectory goals to generate the final trajectory. Our approach presents a new perspective on trajectory prediction. We evaluate TFBNet on five real-world datasets and compare it to state-of-the-art methods. Our results demonstrate that TFBNet achieves significant improvements in the ADE (average displacement error) and FDE (final displacement error) indicators, with increases of 46% and 52%, respectively. These results validate the effectiveness of our proposed approach and its potential to improve the performance of trajectory prediction models in various applications.

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How Can I See My Future? FvTraj: Using First-Person View for Pedestrian Trajectory Prediction

Cited by 15 publications

References 40 publications

Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making

Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making

Driving Scene Understanding: How much temporal context and spatial resolution is necessary?

A Novel Trajectory Feature-Boosting Network for Trajectory Prediction

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