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Sheng Wang - IEEE Xplore Author Profile
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Sheng Wang

Publications
11
Citations
13
Publications by Year
20222024
Co-Authors:
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Sheng Wang

Affiliation

Division of Emerging Interdisciplinary Areas
The Hong Kong University of Science and Technology
Hong Kong SAR, China
Robotics and Autonomous Systems Thrust
The Hong Kong University of Science and Technology (Guangzhou)
Guangzhou, China

Publication Topics

Biography

Sheng Wang (Graduate Student Member, IEEE) received the B.S. degree from the Harbin Institute of Technology, Harbin, China, in 2018, and the M.S. degree from École Centrale de Nantes, Nantes, France, in 2020. He is currently pursuing the Ph.D. degree with the Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, under the supervision of Prof. Ming Liu. His research interests include imitation learning, reinforcement learning, trajectory prediction, and planning.(Based on document published on 13 February 2024).
Publications
11
Citations
13
Publications by Year
20222024
Co-Authors:
Show All Co-Authors (24)
Author's Published Works

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RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment

Ren Xin;Sheng Wang;Yingbing Chen;Jie Cheng;Ming Liu;Jun Ma

2024 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2024 | Conference Paper |
Motion planning is a complicated task that requires the combination of perception, map information integration and prediction, particularly when driving in heavy traffic. Developing an extensible and efficient representation that visualizes sensor noise and provides basis to real-time planning tasks is desirable. We aim to develop an interpretable map representation, which offers prior of driving ...Show More

RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment

Ren Xin;Sheng Wang;Yingbing Chen;Jie Cheng;Ming Liu;Jun Ma

Year: 2024 | Conference Paper |

DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving

Sheng Wang;Ge Sun;Fulong Ma;Tianshuai Hu;Qiang Qin;Yongkang Song;Lei Zhu;Junwei Liang

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
Evaluating and training autonomous driving systems require diverse and scalable corner cases. However, most existing scene generation methods lack controllability, accuracy, and versatility, resulting in unsatisfactory generation results. Inspired by DragGAN in image generation, we propose DragTraffic, a generalized, interactive, and controllable traffic scene generation framework based on conditi...Show More

DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving

Sheng Wang;Ge Sun;Fulong Ma;Tianshuai Hu;Qiang Qin;Yongkang Song;Lei Zhu;Junwei Liang

Year: 2024 | Conference Paper |

DHP-Mapping: A Dense Panoptic Mapping System with Hierarchical World Representation and Label Optimization Techniques

Tianshuai Hu;Jianhao Jiao;Yucheng Xu;Hongji Liu;Sheng Wang;Ming Liu

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structure, we propose DHP-Mapping, a dense mappi...Show More

DHP-Mapping: A Dense Panoptic Mapping System with Hierarchical World Representation and Label Optimization Techniques

Tianshuai Hu;Jianhao Jiao;Yucheng Xu;Hongji Liu;Sheng Wang;Ming Liu

Year: 2024 | Conference Paper |

A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots

Ren Xin;Hongji Liu;Yingbing Chen;Jie Cheng;Sheng Wang;Jun Ma;Ming Liu

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
This paper presents a generic trajectory planning method for wheeled robots with fixed steering axes while the steering angle of each wheel is constrained. In the existing literatures, All-Wheel-Steering (AWS) robots, incorporating modes such as rotation-free translation maneuvers, in-situ rotational maneuvers, and proportional steering, exhibit inefficient performance due to time-consuming mode s...Show More

A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots

Ren Xin;Hongji Liu;Yingbing Chen;Jie Cheng;Sheng Wang;Jun Ma;Ming Liu

Year: 2024 | Conference Paper |

Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions

Sheng Wang;Yingbing Chen;Jie Cheng;Xiaodong Mei;Ren Xin;Yongkang Song;Ming Liu

2024 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose a novel trajectory prediction framework called Partial Observations Prediction (POP) for congested urban road scenarios. The framework consists of two key stages: self-supervised learning (SSL) and feature distillation. ...Show More

Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions

Sheng Wang;Yingbing Chen;Jie Cheng;Xiaodong Mei;Ren Xin;Yongkang Song;Ming Liu

Year: 2024 | Conference Paper |

Enhancing Campus Mobility: Achievements and Challenges of the Snow Lion Autonomous Shuttle

Yingbing Chen;Jie Cheng;Sheng Wang;Hongji Liu;Xiaodong Mei;Xiaoyang Yan;Mingkai Tang;Ge Sun;Ya Wen;Junwei Cai;Xupeng Xie;Lu Gan;Mandan Chao;Ren Xin;Lujia Wang;Ming Liu;Jianhao Jiao

IEEE Robotics & Automation Magazine
Year: 2024 | Early Access Article |
In recent years, the rapid evolution of autonomous vehicles (AVs) has reshaped global transportation systems, leading to an increase in autonomous shuttle applications in people’s daily lives. Leveraging the accomplishments of our earlier endeavor, particularly Hercules [1], an autonomous logistics vehicle for transporting goods, we introduce Snow Lion, an autonomous shuttle vehicle specifically d...Show More

Enhancing Campus Mobility: Achievements and Challenges of the Snow Lion Autonomous Shuttle

Yingbing Chen;Jie Cheng;Sheng Wang;Hongji Liu;Xiaodong Mei;Xiaoyang Yan;Mingkai Tang;Ge Sun;Ya Wen;Junwei Cai;Xupeng Xie;Lu Gan;Mandan Chao;Ren Xin;Lujia Wang;Ming Liu;Jianhao Jiao

Year: 2024 | Early Access Article |

IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

Yingbing Chen;Jie Cheng;Lu Gan;Sheng Wang;Hongji Liu;Xiaodong Mei;Ming Liu

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 8 | Journal Article |
Cited by: Papers (2)
Considerable research efforts have been devoted to the development of motion planning algorithms, which form a cornerstone of the autonomous driving system (ADS). Nonetheless, acquiring an interactive and secure trajectory for the ADS remains challenging due to the complex nature of interaction modeling in planning. Modern planning methods still employ a uniform treatment of prediction outcomes an...Show More

IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

Yingbing Chen;Jie Cheng;Lu Gan;Sheng Wang;Hongji Liu;Xiaodong Mei;Ming Liu

Year: 2024 | Volume: 25, Issue: 8 | Journal Article |

FCUS: Traffic Rule-Aware Vehicle Trajectory Forecasting Using Continuous Unlikelihood and Signal Temporal Logic Feature

Sheng Wang;Ren Xin;Jie Cheng;Xiaodong Mei;Ming Liu

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2023 | Conference Paper |
Vehicle trajectory prediction is essential to autonomous driving tasks. Accurate trajectory prediction of other traffic agents can significantly improve the ability of scene understanding and eventually improve the driving safety level of autonomous vehicles. Popular trajectory prediction methods leverage traffic rules by considering maps or lane graphs as a part of the input. However, this implic...Show More

FCUS: Traffic Rule-Aware Vehicle Trajectory Forecasting Using Continuous Unlikelihood and Signal Temporal Logic Feature

Sheng Wang;Ren Xin;Jie Cheng;Xiaodong Mei;Ming Liu

Year: 2023 | Conference Paper |

Velocity Field: An Informative Traveling Cost Representation for Trajectory Planning

Ren Xin;Jie Cheng;Sheng Wang;Ming Liu

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Trajectory planning involves generating a series of space points to be followed in the near future. However, due to the complex and uncertain nature of the driving environment, it is impractical for autonomous vehicles (AVs) to exhaustively design planning rules for optimizing future trajectories. To address this issue, we propose a local map representation method called Velocity Field. This appro...Show More

Velocity Field: An Informative Traveling Cost Representation for Trajectory Planning

Ren Xin;Jie Cheng;Sheng Wang;Ming Liu

Year: 2023 | Conference Paper |

An Automatic Multi-LiDAR Extrinsic Calibration Algorithm Using Corner Planes

Fulong Ma;Sheng Wang;Ming Liu

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
With the development of autonomous driving, more autonomous vehicles are equipped with multiple sensors to perform better. For multi-sensor systems, accurate extrinsic calibration is a prerequisite for perception and localization systems. Extrinsic calibration aims to transform two or more sensors' coordinates into a unified spatial coordinate system. Sensors usually need to be calibrated after in...Show More

An Automatic Multi-LiDAR Extrinsic Calibration Algorithm Using Corner Planes

Fulong Ma;Sheng Wang;Ming Liu

Year: 2022 | Conference Paper |

MPNP: Multi-Policy Neural Planner for Urban Driving

Jie Cheng;Ren Xin;Sheng Wang;Ming Liu

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2022 | Conference Paper |
Cited by: Papers (6)
Our goal is to train a neural planner that can capture diverse driving behaviors in complex urban scenarios. We observe that even state-of-the-art neural planners are struggling to perform common maneuvers such as lane change, which is rather natural for human drivers. We propose to explore the multi-modalities in the planning problem and force the neural planner to explicitly consider different p...Show More

MPNP: Multi-Policy Neural Planner for Urban Driving

Jie Cheng;Ren Xin;Sheng Wang;Ming Liu

Year: 2022 | Conference Paper |

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