1 citations · 4 across the 21 of their papers we have counts for
6 papers · 1 filter
CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model
Ruoyu Yao, Yubin Wang, Haichao Liu +4
Decision-making and motion planning constitute critical components for ensuring the safety and efficiency of autonomous vehicles (AVs). Existing methodologies typically adopt two p…
Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles
Wenru Liu, Haichao Liu, Lei Zheng +2
This paper introduces a local planner that synergizes the decision making and trajectory planning modules towards autonomous driving. The decision making and trajectory planning ta…
LMMCoDrive: Cooperative Driving with Large Multimodal Model
Haichao Liu, Ruoyu Yao, Zhenmin Huang +2
To address the intricate challenges of decentralized cooperative scheduling and motion planning in Autonomous Mobility-on-Demand (AMoD) systems, this paper introduces LMMCoDrive, a…
Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving
Hang Zhou, Haichao Liu, Hongliang Lu +3
Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction…
Robot Navigation in Unknown and Cluttered Workspace with Dynamical System Modulation in Starshaped Roadmap
Kai Chen, Haichao Liu, Yulin Li +3
Compared to conventional decomposition methods that use ellipses or polygons to represent free space, starshaped representation can better capture the natural distribution of senso…
Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication
Haichao Liu, Zhenmin Huang, Zicheng Zhu +3
This paper investigates a cooperative motion planning problem for large-scale connected autonomous vehicles (CAVs) under limited communications, which addresses the challenges of h…