Collaborative Motion Prediction via Neural Motion Message Passing

Hu, Yue; Chen, Siheng; Zhang, Ya; Gu, Xiao

doi:10.1109/cvpr42600.2020.00635

Cited by 75 publications

(59 citation statements)

References 22 publications

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“…A new trend in representation of the acting agents for neural network models, were graph models [102][103][104][105][106][107][108][109]. Describing the connections between objects in a graph structure in opposition to an occupancy grid, is a huge advantage if there are only sparse connections between the objects.…”

Section: Gnns Attention and New Use Casesmentioning

confidence: 99%

A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

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Towards the aim of mastering level 5, a fully automated vehicle needs to be equipped with sensors for a 360∘ surround perception of the environment. In addition to this, it is required to anticipate plausible evolutions of the traffic scene such that it is possible to act in time, not just to react in case of emergencies. This way, a safe and smooth driving experience can be guaranteed. The complex spatio-temporal dependencies and high dynamics are some of the biggest challenges for scene prediction. The subtile indications of other drivers’ intentions, which are often intuitively clear to the human driver, require data-driven models such as deep learning techniques. When dealing with uncertainties and making decisions based on noisy or sparse data, deep learning models also show a very robust performance. In this survey, a detailed overview of scene prediction models is presented with a historical approach. A quantitative comparison of the model results reveals the dominance of deep learning methods in current state-of-the-art research in this area, leading to a competition on the cm scale. Moreover, it also shows the problem of inter-model comparison, as many publications do not use standardized test sets. However, it is questionable if such improvements on the cm scale are actually necessary. More effort should be spent in trying to understand varying model performances, identifying if the difference is in the datasets (many simple situations versus many corner cases) or actually an issue of the model itself.

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Section: Gnns Attention and New Use Casesmentioning

confidence: 99%

A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

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show abstract

“…The tracking coordinates in this dataset are measured in pixels. We use the same standard data segmentation settings as [47].…”

Section: Experiments and Evaluationmentioning

confidence: 99%

“…RSBG [38]: A recursive social behavior graph combined with GCN is proposed to model social interaction. NMMP [47]: A neural motion message passing is proposed for interactive modeling, which can predict future trajectories in a variety of scenarios. Star [48]: A novel spatial graph transformer is introduced to capture the interaction between pedestrians.…”

Section: B Quantitative Analysismentioning

confidence: 99%

SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction

Lv¹,

Wang²,

Wang³

et al. 2021

Preprint

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Pedestrian trajectory prediction is an important technique of autonomous driving, which has become a research hot-spot in recent years. In order to accurately predict the reasonable future trajectory of pedestrians, it is inevitable to consider social interactions among pedestrians and the influence of surrounding scene simultaneously. Previous methods mainly rely on the position relationship of pedestrians to model social interaction, which is obviously not enough to represent the complex cases in real situations. In addition, most of existing work usually introduce the scene interaction module as an independent branch and embed the social interaction features in the process of trajectory generation, rather than simultaneously carrying out the social interaction and scene interaction, which may undermine the rationality of trajectory prediction. In this paper, we propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN) which aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. In detail, when modeling social interaction, we propose a new social soft attention function, which fully considers various interaction factors among pedestrians. And it can distinguish the influence of pedestrians around the agent based on different factors under various situations. For the physical interaction, we propose one new sequential scene sharing mechanism. The influence of the scene on one agent at each moment can be shared with other neighbors through social soft attention, therefore the influence of the scene is expanded both in spatial and temporal dimension. With the help of these improvements, we successfully obtain socially and physically acceptable predicted trajectories. The experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.

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“…In practice, many graph signals are varying across time, which can be further modeled as timevarying graph signals [2,3]. Those techniques that process time-varying graph signals can be widely used for skeletonbased human action recognition [4], traffic forecasting [5], and multi-agent motion prediction [6].…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Graph Signal Inpainting Via Unrolling Networks

Chen

Eldar

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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We propose an interpretable graph neural network based on algorithm unrolling to reconstruct a time-varying graph signal from partial measurements. The proposed graph unrolling networks expand algorithm unrolling to the graph-time domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative inpainting algorithm by mapping each iteration to a single network layer. The feed-forward process is thus equivalent to iteratively reconstructing a time-varying graph signal. We train this network through unsupervised learning, where the input time-varying graph signal is used to supervise the training. By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input data, instead of manually choosing signal priors. To validate the proposed methods, we conduct experiments on three real-world datasets and demonstrate that our networks achieve smaller reconstruction errors than conventional inpainting algorithms and state-of-the-art graph neural networks.

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Collaborative Motion Prediction via Neural Motion Message Passing

Cited by 75 publications

References 22 publications

A Review on Scene Prediction for Automated Driving

A Review on Scene Prediction for Automated Driving

SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction

Time-Varying Graph Signal Inpainting Via Unrolling Networks

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