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20182024
most citedPORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians

13 citations · 57 across the 11 of their papers we have counts for

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9 papers · 1 filter

cs.RO2024

Neural Randomized Planning for Whole Body Robot Motion

Yunfan Lu, Yuchen Ma, David Hsu +1

Robot motion planning has made vast advances over the past decades, but the challenge remains: robot mobile manipulators struggle to plan long-range whole-body motion in common hou…

cs.RO2023★ 8 cited

What Truly Matters in Trajectory Prediction for Autonomous Driving?

Phong Tran, Haoran Wu, Cunjun Yu +3

Trajectory prediction plays a vital role in the performance of autonomous driving systems, and prediction accuracy, such as average displacement error (ADE) or final displacement e…

cs.RO2021★ 2 cited

Closing the Planning-Learning Loop with Application to Autonomous Driving

Panpan Cai, David Hsu

Real-time planning under uncertainty is critical for robots operating in complex dynamic environments. Consider, for example, an autonomous robot vehicle driving in dense, unregula…

cs.RO2020★ 3 cited

Simulating Autonomous Driving in Massive Mixed Urban Traffic

Yuanfu Luo, Panpan Cai, Yiyuan Lee +1

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents S…

cs.RO2020

MAGIC: Learning Macro-Actions for Online POMDP Planning

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 computa…

cs.RO2019★ 8 cited

SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic

Panpan Cai, Yiyuan Lee, Yuanfu Luo +1

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents S…

cs.RO2019★ 4 cited

GAMMA: A General Agent Motion Model for Autonomous Driving

Yuanfu Luo, Panpan Cai, Yiyuan Lee +1

This paper presents GAMMA, a general motion prediction model that enables large-scale real-time simulation and planning for autonomous driving. GAMMA models heterogeneous, interact…

cs.RO2019★ 8 cited

LeTS-Drive: Driving in a Crowd by Learning from Tree Search

Panpan Cai, Yuanfu Luo, Aseem Saxena +2

Autonomous driving in a crowded environment, e.g., a busy traffic intersection, is an unsolved challenge for robotics. The robot vehicle must contend with a dynamic and partially o…

cs.RO2018★ 13 cited

PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians

Yuanfu Luo, Panpan Cai, Aniket Bera +3

This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is PORCA, a pedestrian motion prediction model that accounts f…