14 citations · 36 across the 10 of their papers we have counts for
10 papers
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…
How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?
Wenbo Shao, Yanchao Xu, Jun Li +3
An accurate trajectory prediction is crucial for safe and efficient autonomous driving in complex traffic environments. In recent years, artificial intelligence has shown strong ca…
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…