GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving

Abstract

Despite the advancement in the technology of autonomous driving cars, 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. Previously, 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. Even though our experimental results show that GRIP improves the prediction accuracy of the state-of-the-art solution by 30%, GRIP still has some limitations. GRIP uses a fixed graph to describe the relationships between different traffic agents and hence may suffer some performance degradations when it is being used in urban traffic scenarios. Hence, in this paper, we describe an improved scheme called GRIP++ where we use both fixed and dynamic graphs for trajectory predictions of different types of traffic agents. Such an improvement can help autonomous driving cars avoid many traffic accidents. Our evaluations using a recently released urban traffic dataset, namely ApolloScape showed that GRIP++ achieves better prediction accuracy than state-of-the-art schemes. GRIP++ ranked #1 on the leaderboard of the ApolloScape trajectory competition in October 2019. In addition, GRIP++ runs 21.7 times faster than a state-of-the-art scheme, CS-LSTM.

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Cited by in corpus (6)

• Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
• Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
• Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning
• Relation-based Motion Prediction using Traffic Scene Graphs
• Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information
• UST: Unifying Spatio-Temporal Context for Trajectory Prediction in Autonomous Driving