no code implementations • 12 Mar 2023 • Yiyuan Lee, Katie Lee, Panpan Cai, David Hsu, Lydia E. Kavraki
Identifying internal parameters for planning is crucial to maximizing the performance of a planner.
1 code implementation • 23 Sep 2022 • Mohamad H. Danesh, Panpan Cai, David Hsu
To address this, we propose a new algorithm, LEarning Attention over Driving bEhavioRs (LEADER), that learns to attend to critical human behaviors during planning.
no code implementations • 11 Jan 2021 • Panpan Cai, David Hsu
To achieve real-time performance for large-scale planning, this work introduces a new algorithm Learning from Tree Search for Driving (LeTS-Drive), which integrates planning and learning in a closed loop, and applies it to autonomous driving in crowded urban traffic in simulation.
Autonomous Driving Robotics
1 code implementation • 7 Nov 2020 • Yiyuan Lee, Panpan Cai, David Hsu
The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required.
3 code implementations • 11 Nov 2019 • Panpan Cai, Yiyuan Lee, Yuanfu Luo, David Hsu
Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants.
Robotics Multiagent Systems
1 code implementation • 4 Jun 2019 • Yuanfu Luo, Panpan Cai, Yiyuan Lee, David Hsu
Further, the computational efficiency and the flexibility of GAMMA enable (i) simulation of mixed urban traffic at many locations worldwide and (ii) planning for autonomous driving in dense traffic with uncertain driver behaviors, both in real-time.
no code implementations • 29 May 2019 • Panpan Cai, Yuanfu Luo, Aseem Saxena, David Hsu, Wee Sun Lee
LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both.
no code implementations • 30 May 2018 • Yuanfu Luo, Panpan Cai, Aniket Bera, David Hsu, Wee Sun Lee, Dinesh Manocha
Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time.
Robotics
1 code implementation • 17 Feb 2018 • Panpan Cai, Yuanfu Luo, David Hsu, Wee Sun Lee
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost.