40 citations · 82 across the 12 of their papers we have counts for
12 papers
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…
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou +12
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer m…
Hybrid-Prediction Integrated Planning for Autonomous Driving
Haochen Liu, Zhiyu Huang, Wenhui Huang +3
Autonomous driving systems require the ability to fully understand and predict the surrounding environment to make informed decisions in complex scenarios. Recent advancements in l…
A Generalized Multi-Modal Fusion Detection Framework
Leichao Cui, Xiuxian Li, Min Meng +1
LiDAR point clouds have become the most common data source in autonomous driving. However, due to the sparsity of point clouds, accurate and reliable detection cannot be achieved i…
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving
Haochen Liu, Zhiyu Huang, Xiaoyu Mo +1
Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures. Although reinfor…
ReCoAt: A Deep Learning-based Framework for Multi-Modal Motion Prediction in Autonomous Driving Application
Zhiyu Huang, Xiaoyu Mo, Chen Lv
This paper proposes a novel deep learning framework for multi-modal motion prediction. The framework consists of three parts: recurrent neural networks to process the target agent'…
Multi-task Driver Steering Behaviour Modeling Using Time-Series Transformer
Yang Xing, Wenbo Li, Xiaoyu Mo +1
Human intention prediction provides an augmented solution for the design of assistants and collaboration between the human driver and intelligent vehicles. In this study, a multi-t…
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…