activity
20202024
most citedMulti-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding

189 citations · 261 across the 17 of their papers we have counts for

collaborators

21 papers

cs.RO2024

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…

cs.RO2024★ 1 cited

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…

cs.RO2024

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…

cs.RO2024

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…

cs.RO2023

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…

cs.RO2023

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…