Potential Field: Interpretable and Unified Representation for Trajectory Prediction

Su, Shan; Cheng, Peng; Shi, Jianbo; Choi, Chiho

doi:10.48550/arxiv.1911.07414

Cited by 3 publications

(1 citation statement)

References 36 publications

(42 reference statements)

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“…2) determines motion behavior of the target agent with respect to other directly and indirectly observable influences. We borrow the notion of stimuli in [30] to categorize such influences into three types as in [31]. Inertial stimuli find motion intent of the target agent by observing the sequence of its past state such as positions.…”

Section: Latent Spacementioning

confidence: 99%

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

Choi¹,

Choi²,

Malla³

et al. 2020

Preprint

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We propose a framework for predicting future trajectories of traffic agents in highly interactive environments. On the basis of the fact that autonomous driving vehicles are equipped with various types of sensors (e.g., LiDAR scanner, RGB camera, etc.), our work aims to get benefit from the use of multiple input modalities that are complementary to each other. The proposed approach is composed of two stages. (i) feature encoding where we discover motion behavior of the target agent with respect to other directly and indirectly observable influences. We extract such behaviors from multiple perspectives such as in top-down and frontal view. (ii) cross-modal embedding where we embed a set of learned behavior representations into a single cross-modal latent space. We construct a generative model and formulate the objective functions with an additional regularizer specifically designed for future prediction. An extensive evaluation is conducted to show the efficacy of the proposed framework using two benchmark driving datasets.

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Section: Latent Spacementioning

confidence: 99%

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

Choi¹,

Choi²,

Malla³

et al. 2020

Preprint

Self Cite

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EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning

Yang

Tomizuka

et al. 2020

Preprint

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Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking

Zhang

et al. 2021

Preprint

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An effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (e.g. autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios. Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent. In this paper, we propose a generic generative neural system (called STG-DAT) for multi-agent trajectory prediction involving heterogeneous agents. The system takes a step forward to explicit interaction modeling by incorporating relational inductive biases with a dynamic graph representation and leverages both trajectory and scene context information. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction. The constraint not only ensures physical feasibility but also enhances model performance. Moreover, the proposed prediction model can be easily adopted by multi-target tracking frameworks. The tracking accuracy proves to be improved by empirical results. The proposed system is evaluated on three public benchmark datasets for trajectory prediction, where the agents cover pedestrians, cyclists and on-road vehicles. The experimental results demonstrate that our model achieves better performance than various baseline approaches in terms of prediction and tracking accuracy.

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Potential Field: Interpretable and Unified Representation for Trajectory Prediction

Cited by 3 publications

References 36 publications

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning

Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking

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