Proceedings Article10.1109/icra46639.2022.9811567
A Deep Concept Graph Network for Interaction-Aware Trajectory Prediction
Yutong Ban,Xiao Li,Guy Rosman,Igor Gilitschenski,Ozanan R. Meireles,Sertac Karaman,Daniela Rus +6 more
- 23 May 2022
pp 8992-8998
11
TL;DR: The ConceptNet trajectory predictor is proposed - a novel prediction framework that is able to incorporate agent interactions as explicit edges in a temporal knowledge graph and it is shown that using the graphical structure to explicitly model interactions enables better detection of agent interactions and improved trajectory predictions on a large real-world driving dataset.
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Abstract: Temporal patterns (how vehicles behave in our observed past) underline our reasoning of how people drive on the road, and can explain why we make certain predictions about interactions among road agents. In this paper we propose the ConceptNet trajectory predictor - a novel prediction framework that is able to incorporate agent interactions as explicit edges in a temporal knowledge graph. We demonstrate the sample efficiency and the overall accuracy of the proposed approach, and show that using the graphical structure to explicitly model interactions enables better detection of agent interactions and improved trajectory predictions on a large real-world driving dataset.
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Citations
Incorporating Driving Knowledge in Deep Learning Based Vehicle Trajectory Prediction: A Survey
TL;DR: In this paper , the authors systematically investigated the research status of DL-based VTP, and summarized the application methods and application stages of driving knowledge in DL-Based VTP.
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FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs
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- 01 Jun 2023
TL;DR: FJMP is a framework for factored joint multi-agent motion prediction over learned directed acyclic interaction graphs. It models future scene interaction dynamics as a sparse directed interaction graph and produces more accurate and scene-consistent joint trajectory predictions than non-factorized approaches.
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P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
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- 01 Oct 2023
TL;DR: P4P is a conflict-aware motion prediction system that identifies conflicts between autonomous vehicles and other traffic agents with high accuracy. However, existing motion predictors often fail to identify conflicts effectively, leading to a large percentage of collisions. P4P addresses this issue by combining a physics-based trajectory generator and a learning-based relation predictor to identify conflicts and infer conflict relations.
2
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•Posted Content
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