509 citations · 1.1k across the 28 of their papers we have counts for
7 papers · 2 filters
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…
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving
Jingda Wu, Zhiyu Huang, Chen Lv
To further improve the learning efficiency and performance of reinforcement learning (RL), in this paper we propose a novel uncertainty-aware model-based RL (UA-MBRL) framework, an…
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…
Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving
Jingda Wu, Zhiyu Huang, Chao Huang +4
Due to the limited smartness and abilities of machine intelligence, currently autonomous vehicles are still unable to handle all kinds of situations and completely replace drivers.…
Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving
Zhiyu Huang, Jingda Wu, Chen Lv
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous driving. However, the low sample efficiency and difficulty of designing reward functions for D…
Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving
Haochen Liu, Zhiyu Huang, Jingda Wu +1
Learning-based approaches, such as reinforcement learning (RL) and imitation learning (IL), have indicated superiority over rule-based approaches in complex urban autonomous drivin…