Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs

We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level information (future trajectories) as well as the high-level information (road-agent behavior) from the extracted trajectory o...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2020-07, Vol.5 (3), p.4882-4890
Main Authors: Chandra, Rohan, Guan, Tianrui, Panuganti, Srujan, Mittal, Trisha, Bhattacharya, Uttaran, Bera, Aniket, Manocha, Dinesh
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Approximation
autonomous agents
Clustering
Error reduction
Forecasting
Intelligent transportation systems
Machine learning
Prediction algorithms
Predictions
Predictive models
Reagents
Regularization
Roads
Signal processing algorithms
Spectra
Trajectory
Upper bounds
Vehicle dynamics
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Summary:We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level information (future trajectories) as well as the high-level information (road-agent behavior) from the extracted trajectory of each road-agent. Our formulation represents the proximity between the road agents using a weighted dynamic geometric graph (DGG). We use a two-stream graph-LSTM network to perform traffic forecasting using these weighted DGGs. The first stream predicts the spatial coordinates of road-agents, while the second stream predicts whether a road-agent is going to exhibit overspeeding, underspeeding, or neutral behavior by modeling spatial interactions between road-agents. Additionally, we propose a new regularization algorithm based on spectral clustering to reduce the error margin in long-term prediction (3-5 seconds) and improve the accuracy of the predicted trajectories. Moreover, we prove a theoretical upper bound on the regularized prediction error. We evaluate our approach on the Argoverse, Lyft, Apolloscape, and NGSIM datasets and highlight the benefits over prior trajectory prediction methods. In practice, our approach reduces the average prediction error by approximately 75% over prior algorithms and achieves a weighted average accuracy of 91.2% for behavior prediction. Additionally, our spectral regularization improves long-term prediction by up to 70%.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.3004794