14 citations · 29 across the 14 of their papers we have counts for
14 papers
HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning
Xiaodong Mei, Sheng Wang, Jie Cheng +2
Motion forecasting represents a critical challenge in autonomous driving systems, requiring accurate prediction of surrounding agents' future trajectories. While existing approache…
Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection and Simulation
Yikang Zhang, Chuang-Wei Liu, Jiahang Li +3
Road inspection is crucial for maintaining road serviceability and ensuring traffic safety, as road defects gradually develop and compromise functionality. Traditional inspection m…
Velocity Field: An Informative Traveling Cost Representation for Trajectory Planning
Ren Xin, Jie Cheng, Sheng Wang +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…
RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment
Ren Xin, Sheng Wang, Yingbing Chen +3
Motion planning is a complicated task that requires the combination of perception, map information integration and prediction, particularly when driving in heavy traffic. Developin…
PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving
Jie Cheng, Yingbing Chen, Qifeng Chen
We present PLUTO, a powerful framework that pushes the limit of imitation learning-based planning for autonomous driving. Our improvements stem from three pivotal aspects: a longit…
A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots
Ren Xin, Hongji Liu, Yingbing Chen +4
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 literatu…
Efficient Global Navigational Planning in 3D Structures based on Point Cloud Tomography
Bowen Yang, Jie Cheng, Bohuan Xue +2
Navigation in complex 3D scenarios requires appropriate environment representation for efficient scene understanding and trajectory generation. We propose a highly efficient and ex…
Enhancing Campus Mobility: Achievements and Challenges of Autonomous Shuttle "Snow Lion''
Yingbing Chen, Jie Cheng, Sheng Wang +15
The rapid evolution of autonomous vehicles (AVs) has significantly influenced global transportation systems. In this context, we present ``Snow Lion'', an autonomous shuttle meticu…
IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning
Yingbing Chen, Jie Cheng, Lu Gan +4
Considerable research efforts have been devoted to the development of motion planning algorithms, which form a cornerstone of the autonomous driving system (ADS). Nonetheless, acqu…
Rethinking Imitation-based Planner for Autonomous Driving
Jie Cheng, Yingbing Chen, Xiaodong Mei +3
In recent years, imitation-based driving planners have reported considerable success. However, due to the absence of a standardized benchmark, the effectiveness of various designs…
Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions
Sheng Wang, Yingbing Chen, Jie Cheng +4
Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose…
Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders
Jie Cheng, Xiaodong Mei, Ming Liu
This study explores the application of self-supervised learning (SSL) to the task of motion forecasting, an area that has not yet been extensively investigated despite the widespre…
Efficient Speed Planning for Autonomous Driving in Dynamic Environment with Interaction Point Model
Yingbing Chen, Ren Xin, Jie Cheng +4
Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are…
Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement
Cheng Jie, Chen Yingbing, Zhang Qingwen +2
Real-time kinodynamic trajectory planning in dynamic environments is critical yet challenging for autonomous driving. In this letter, we propose an efficient trajectory planning sy…