120 citations · 206 across the 14 of their papers we have counts for
8 papers · 1 filter
OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching
Qihang Huang, Wenhui Huang, Collister Chua +6
Occupancy world models provide a structured, metric representation of future driving scenes, making them a promising basis for end-to-end autonomous driving. However, existing occu…
GeoWorldAD: Geometry World Action Model for Autonomous Driving
Songyan Zhang, Jinyuan Tian, Hanbing Li +9
Autonomous driving requires both safe and efficient planning decisions in dynamic 3D environments. Although recent Vision/Video-Action models learn policies directly from visual ob…
MoTVLA: A Vision-Language-Action Model with Unified Fast-Slow Reasoning
Wenhui Huang, Changhe Chen, Han Qi +3
Integrating visual-language instructions into visuomotor policies is gaining momentum in robot learning for enhancing open-world generalization. Despite promising advances, existin…
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 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…
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation
Wenhui Huang, Yanxin Zhou, Xiangkun He +1
Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One…
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…
Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning
Wenhui Huang, Francesco Braghin, Zhuo Wang
With the implementation of reinforcement learning (RL) algorithms, current state-of-art autonomous vehicle technology have the potential to get closer to full automation. However,…