GRIP: Graph-based Interaction-aware Trajectory Prediction

By Xin Li, Xiaowen Ying, Mooi Choo Choo Chuah. International Conference on Intelligent Transportation Systems, 2019

300 citations recorded by the source corpus.

Nowadays, autonomous driving cars have become commercially available. However, the safety of a self-driving car is still a challenging problem that has not been well studied. Motion prediction is one of the core functions of an autonomous driving car. In this paper, we propose a novel scheme called GRIP which is designed to predict trajectories for traffic agents around an autonomous car efficiently. GRIP uses a graph to represent the interactions of close objects, applies several graph convolutional blocks to extract features, and subsequently uses an encoder-decoder long short-term memory (LSTM) model to make predictions. The experimental results on two well-known public datasets show that our proposed model improves the prediction accuracy of the state-of-the-art solution by 30%. The prediction error of GRIP is one meter shorter than existing schemes. Such an improvement can help autonomous driving cars avoid many traffic accidents. In addition, the proposed GRIP runs 5x faster than the state-of-the-art schemes.

Subfields: Automotive Engineering, Building and Construction, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/ff835d571253fd167a55c4a1d5da48ed1f8abe5d

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Citations in the corpus

A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, methodology): “In the space-based graph convolutional network, a GCN-based trajectory prediction model called GRIP is proposed by Li et al. [127], which treats each vehicle as a node at each sampling time and considers the interaction-related factors.”
Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, unknown): “[120] argued that the previous RNN and CNNbased methods for trajectory prediction only focus on one vehicle and ignore the interactions among adjacent objects.”
Core Challenges of Social Robot Navigation: A Survey (2021, background): “Note that recent research in neighboring fields such as autonomous driving and human motion prediction is also dominated by approaches that strive to leverage models of coupled interaction (Tang and Salakhutdinov 2019; Roh et al. 2020; Li et al. 2019; Chandra et al. 2019; Mavrogiannis et al. 2020; L”