40 citations · 70 across the 7 of their papers we have counts for
6 papers · 1 filter
SparScene: Efficient Traffic Scene Representation via Sparse Graph Learning for Large-Scale Trajectory Generation
Xiaoyu Mo, Jintian Ge, Zifan Wang +2
Multi-agent trajectory generation is a core problem for autonomous driving and intelligent transportation systems. However, efficiently modeling the dynamic interactions between nu…
Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving
Zhiyu Huang, Xiaoyu Mo, Chen Lv
Predicting the behaviors of other agents on the road is critical for autonomous driving to ensure safety and efficiency. However, the challenging part is how to represent the socia…
Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving
Xiaoyu Mo, Yang Xing, Chen Lv
Integrating trajectory prediction to the decision-making and planning modules of modular autonomous driving systems is expected to improve the safety and efficiency of self-driving…
Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction
Xiaoyu Mo, Yang Xing, Chen Lv
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for the safe and efficient operation of connected automated vehicles under complex d…
ReCoG: A Deep Learning Framework with Heterogeneous Graph for Interaction-Aware Trajectory Prediction
Xiaoyu Mo, Yang Xing, Chen Lv
Predicting the future trajectory of surrounding vehicles is essential for the navigation of autonomous vehicles in complex real-world driving scenarios. It is challenging as a vehi…
Interaction-Aware Trajectory Prediction of Connected Vehicles using CNN-LSTM Networks
Xiaoyu Mo, Yang Xing, Chen Lv
Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the basic abilities of an autonomous vehicle. In congestion, a vehicle's future movement is…