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Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
[article]
2024
arXiv
pre-print
Preserved Fulltext
As a result, many recent works have proposed methods for estimating HD maps online from sensor data, enabling AVs to operate outside of previously-mapped regions. ...
In doing so, we find that incorporating uncertainty yields up to 50% faster training convergence and up to 15% better prediction performance on the real-world nuScenes driving dataset. ...
Multi-agent visualizations. Red indicates the GT and pink shows future agent predictions. In all three scenarios, our approach produces sensible predictions for both ego and non-ego agents. ...
arXiv:2403.16439v1
fatcat:n4ayos7l7na23hhd7hlwjupr6i
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction
[article]
2023
arXiv
pre-print
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In this paper, we propose a hierarchical hybrid framework of deep learning (DL) and reinforcement learning (RL) for multi-agent trajectory prediction, to cope with the challenge of predicting motions shaped ...
Accurate and robust trajectory prediction of neighboring agents is critical for autonomous vehicles traversing in complex scenes. ...
In this paper, we propose a hierarchical hybrid framework of deep learning (DL) and reinforcement learning (RL) for multi-agent trajectory prediction, to cope with the challenge of predicting motions shaped ...
arXiv:2303.12274v3
fatcat:absesu7ebbgntbnbfgzu3jryai
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles
[article]
2023
arXiv
pre-print
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Deep learning is the method of choice for trajectory prediction for autonomous vehicles. ...
Besides, uncertainty-awareness becomes increasingly important for safety-crucial cyber physical systems whose prediction module heavily relies on machine learning tools. ...
HiVT: Hierarchical Vector Transformer Hierarchical Vector Transformer (HiVT) is a centralized, lightweight, and graph-based motion prediction model [6] . ...
arXiv:2303.04340v1
fatcat:gs6irckppbhzxcs744j3e3gtru
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
[article]
2023
arXiv
pre-print
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The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future trajectories of surrounding ...
Then, based on KNARPE we present the Heterogeneous Polyline Transformer with Relative pose encoding (HPTR), a hierarchical framework enabling asynchronous token update during the online inference. ...
HiVT augments the agent-centric encoders with a pairwise-relative interaction decoder in order to realize multi-agent prediction. ...
arXiv:2310.12970v1
fatcat:edirou2xhzaapot67tdqwsa7vi
On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2308.16539v1 fatcat:ykpgp7wqi5eeroxgodbyinaxpi
Game theory offers an interpretable mathematical framework for modeling multi-agent interactions. ...
However, its applicability in real-world robotics applications is hindered by several challenges, such as unknown agents' preferences and goals. ...
-AI-based Situation Interpretation for Automated Driving. ...
arXiv:2308.16539v2
fatcat:7if6wr4zufanrmgiv4hkapi6gm
SmartRefine: An Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
[article]
2024
arXiv
pre-print
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Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. ...
Context information, such as road maps and surrounding agents' states, provides crucial geometric and semantic information for motion behavior prediction. ...
Inspired by human hierarchical decision-making where the motion intention determines the specific trajectory, goal-conditional prediction, which first predicts or predefines goal candidates, and then predicts ...
arXiv:2403.11492v1
fatcat:g5tormk3rfaarpisdueciq4tvu
SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
[article]
2024
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
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Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. ...
Context information, such as road maps and surrounding agents' states, provides crucial geometric and semantic information for motion behavior prediction. ...
Inspired by human hierarchical decision-making where the motion intention determines the specific trajectory, goal-conditional prediction, which first predicts or predefines goal candidates, and then predicts ...
arXiv:2403.11492v2
fatcat:o6bklyqczvbjneql6nayrkjcva
R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement
[article]
2023
arXiv
pre-print
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Predicting the future motion of dynamic agents is of paramount importance to ensuring safety and assessing risks in motion planning for autonomous robots. ...
In this study, we propose a two-stage motion prediction method, called R-Pred, designed to effectively utilize both scene and interaction context using a cascade of the initial trajectory proposal and ...
Each dynamic agent plans its motion by predicting the future motion and behavior of other agents around it. ...
arXiv:2211.08609v6
fatcat:2ezhbchxtncojegmbbtrl42h6a
HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding
[article]
2023
arXiv
pre-print
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Experimental results show that HDGT achieves state-of-the-art performance for the task of trajectory prediction, on INTERACTION Prediction Challenge and Waymo Open Motion Challenge. ...
Encoding a driving scene into vector representations has been an essential task for autonomous driving that can benefit downstream tasks e.g. trajectory prediction. ...
The authors are thankful to the anonymous reviewers for their valuable comments. ...
arXiv:2205.09753v2
fatcat:sksde6buwvamzg7ljyyllgbkdq
Traj-MAE: Masked Autoencoders for Trajectory Prediction
[article]
2023
arXiv
pre-print
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To overcome the challenge, we propose an efficient masked autoencoder for trajectory prediction (Traj-MAE) that better represents the complicated behaviors of agents in the driving environment. ...
Our experimental results in both multi-agent and single-agent settings demonstrate that Traj-MAE achieves competitive results with state-of-the-art methods and significantly outperforms our baseline model ...
To further demonstrate that our Traj-MAE has the ability to capture the social and temporal information for multi-agent trajectory prediction. ...
arXiv:2303.06697v1
fatcat:ze3r5bzwojephiq7amoux7zxle
FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs
[article]
2022
arXiv
pre-print
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To this end, we propose FJMP, a Factorized Joint Motion Prediction framework for multi-agent interactive driving scenarios. ...
Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. ...
Hivt: Hierarchical vector transformer for multi-agent motion prediction. ...
arXiv:2211.16197v1
fatcat:psd4va4f3jeyrpuo2cafo54x3q
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
2023
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future trajectories of surrounding ...
However, they suffer from high computational overhead and poor scalability as the number of agents to be predicted increases. ...
HiVT augments the agent-centric encoders with a pairwise-relative interaction decoder in order to realize multi-agent prediction. ...
doi:10.3929/ethz-b-000643424
fatcat:5hpmlcrdmnduzkr4bo2aulqgr4