189 citations · 390 across the 14 of their papers we have counts for
14 papers
Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving
Zhiyu Huang, Haochen Liu, Jingda Wu +2
Predicting the behaviors of other road users is crucial to safe and intelligent decision-making for autonomous vehicles (AVs). However, most motion prediction models ignore the inf…
Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving
Zhiyu Huang, Haochen Liu, Jingda Wu +1
Making safe and human-like decisions is an essential capability of autonomous driving systems, and learning-based behavior planning presents a promising pathway toward achieving th…
Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy Traffic: Framework, Survey, and Challenges
Qi Liu, Xueyuan Li, Zirui Li +5
Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully au…
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning
Jingda Wu, Wenhui Huang, Niels de Boer +3
Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus the…
Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving
Zhiyu Huang, Haochen Liu, Jingda Wu +1
Predicting the future states of surrounding traffic participants and planning a safe, smooth, and socially compliant trajectory accordingly is crucial for autonomous vehicles. Ther…
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
Wenhui Huang, Cong Zhang, Jingda Wu +3
Massive practical works addressed by Deep Q-network (DQN) algorithm have indicated that stochastic policy, despite its simplicity, is the most frequently used exploration approach.…
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments
Qi Liu, Zirui Li, Xueyuan Li +2
An efficient and reliable multi-agent decision-making system is highly demanded for the safe and efficient operation of connected autonomous vehicles in intelligent transportation…
Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving
Jingda Wu, Zhiyu Huang, Wenhui Huang +1
Reinforcement learning (RL) requires skillful definition and remarkable computational efforts to solve optimization and control problems, which could impair its prospect. Introduci…
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…
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…
Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning
Zhiyu Huang, Jingda Wu, Chen Lv
Driving behavior modeling is of great importance for designing safe, smart, and personalized autonomous driving systems. In this paper, an internal reward function-based driving mo…
Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding
Zhiyu Huang, Chen Lv, Yang Xing +1
This study aims to improve the performance and generalization capability of end-to-end autonomous driving with scene understanding leveraging deep learning and multimodal sensor fu…