Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction

06/14/2021
by   Xiaoyu Mo, et al.
0

Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for the safe and efficient operation of connected automated vehicles under complex driving situations in the real world. The multi-agent prediction task is challenging, as the motions of traffic participants are affected by many factors, including their individual dynamics, their interactions with surrounding agents, the traffic infrastructures, and the number and modalities of the target agents. To further advance the trajectory prediction techniques, in this work we propose a three-channel framework together with a novel Heterogeneous Edge-enhanced graph ATtention network (HEAT), which is able to deal with the heterogeneity of the target agents and traffic participants involved. Specifically, the agent's dynamics are extracted from their historical states using type-specific encoders. The inter-agent interactions are represented with a directed edge-featured heterogeneous graph, and then interaction features are extracted using the proposed HEAT network. Besides, the map features are shared across all agents by introducing a selective gate mechanism. And finally, the trajectories of multi-agent are executed simultaneously. Validations using both urban and highway driving datasets show that the proposed model can realize simultaneous trajectory predictions for multiple agents under complex traffic situations, and achieve state-of-the-art performance with respect to prediction accuracy, demonstrating its feasibility and effectiveness.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

04/09/2019

Multi-Agent Tensor Fusion for Contextual Trajectory Prediction

Accurate prediction of others' trajectories is essential for autonomous ...
07/31/2021

Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction

Understanding complex social interactions among agents is a key challeng...
03/08/2022

Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction

Multi-agent interactions are important to model for forecasting other ag...
07/08/2021

Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving

Integrating trajectory prediction to the decision-making and planning mo...
03/31/2020

EvolveGraph: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction with Evolving Interaction Graphs

Multi-agent interacting systems are prevalent in the world, from pure ph...
07/06/2020

Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism

The trajectory prediction is significant for the decision-making of auto...
02/12/2021

SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs

Autonomous vehicles navigate in dynamically changing environments under ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.