SSP: Single Shot Future Trajectory Prediction

Dwivedi, Isht; Malla, Srikanth; Dariush, Behzad; Choi, Chiho

doi:10.1109/iros45743.2020.9340754

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…With the recent success of deep networks, RNN-based approaches have become prevalent. These works propose to model interactions among multiple agents by applying aggregation functions on their RNN hidden states [1,15,19], running convolutional layers on agents' spatial feature maps [5,10,64,58], or leveraging attention mechanisms or relational reasoning on constructed graphs of agents [27,50,51,63,57]. Some recent studies are, however, rethinking the use of RNN and social information in modeling temporal dependencies and borrowing the idea of transformers into the area [13].…”

Section: Related Workmentioning

confidence: 99%

Trajectory Prediction with Latent Belief Energy-Based Model

Pang¹,

Zhao²,

Xie³

et al. 2021

Preprint

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Human trajectory prediction is critical for autonomous platforms like self-driving cars or social robots. We present a latent belief energy-based model (LB-EBM) for diverse human trajectory forecast. LB-EBM is a probabilistic model with cost function defined in the latent space to account for the movement history and social context. The lowdimensionality of the latent space and the high expressivity of the EBM make it easy for the model to capture the multimodality of pedestrian trajectory distributions. LB-EBM is learned from expert demonstrations (i.e., human trajectories) projected into the latent space. Sampling from or optimizing the learned LB-EBM yields a belief vector which is used to make a path plan, which then in turn helps to predict a long-range trajectory. The effectiveness of LB-EBM and the two-step approach are supported by strong empirical results. Our model is able to make accurate, multi-modal, and social compliant trajectory predictions and improves over prior state-of-the-arts performance on the Stanford Drone trajectory prediction benchmark by 10.9% and on the ETH-UCY benchmark by 27.6%.

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