189 citations · 407 across the 21 of their papers we have counts for
21 papers
Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
Wenru Liu, Yongkang Song, Chengzhen Meng +4
We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning. Specifically, we form…
Versatile Behavior Diffusion for Generalized Traffic Agent Simulation
Zhiyu Huang, Zixu Zhang, Ameya Vaidya +3
Existing traffic simulation models often fall short in capturing the intricacies of real-world scenarios, particularly the interactive behaviors among multiple traffic participants…
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…
Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving
Zhiyu Huang, Chen Tang, Chen Lv +2
Effective decision-making in autonomous driving relies on accurate inference of other traffic agents' future behaviors. To achieve this, we propose an online belief-update-based be…
DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving
Zhiyu Huang, Peter Karkus, Boris Ivanovic +3
Motion prediction and cost evaluation are vital components in the decision-making system of autonomous vehicles. However, existing methods often ignore the importance of cost learn…
Occupancy Prediction-Guided Neural Planner for Autonomous Driving
Haochen Liu, Zhiyu Huang, Chen Lv
Forecasting the scalable future states of surrounding traffic participants in complex traffic scenarios is a critical capability for autonomous vehicles, as it enables safe and fea…
GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving
Zhiyu Huang, Haochen Liu, Chen Lv
Autonomous vehicles operating in complex real-world environments require accurate predictions of interactive behaviors between traffic participants. This paper tackles the interact…
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…
STrajNet: Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving
Haochen Liu, Zhiyu Huang, Chen Lv
Forecasting the future states of surrounding traffic participants is a crucial capability for autonomous vehicles. The recently proposed occupancy flow field prediction introduces…
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'…
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…
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…
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…