187 citations · 225 across the 18 of their papers we have counts for
13 papers · 1 filter
A Hierarchical Multi-Vehicle Coordinated Motion Planning Method based on Interactive Spatio-Temporal Corridors
Xiang Zhang, Boyang Wang, Yaomin Lu +3
Multi-vehicle coordinated motion planning has always been challenged to safely and efficiently resolve conflicts under non-holonomic dynamic constraints. Constructing spatial-tempo…
Continual Interactive Behavior Learning With Traffic Divergence Measurement: A Dynamic Gradient Scenario Memory Approach
Yunlong Lin, Zirui Li, Cheng Gong +3
Developing autonomous vehicles (AVs) helps improve the road safety and traffic efficiency of intelligent transportation systems (ITS). Accurately predicting the trajectories of tra…
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery
Xianqi He, Lin Yang, Chao Lu +2
In urban environments, the complex and uncertain intersection scenarios are challenging for autonomous driving. To ensure safety, it is crucial to develop an adaptive decision maki…
An Ensemble Learning Framework for Vehicle Trajectory Prediction in Interactive Scenarios
Zirui Li, Yunlong Lin, Cheng Gong +4
Precisely modeling interactions and accurately predicting trajectories of surrounding vehicles are essential to the decision-making and path-planning of intelligent vehicles. This…
Driver-Specific Risk Recognition in Interactive Driving Scenarios using Graph Representation
Jinghang Li, Chao Lu, Penghui Li +3
This paper presents a driver-specific risk recognition framework for autonomous vehicles that can extract inter-vehicle interactions. This extraction is carried out for urban drivi…
Prediction of Pedestrian Spatiotemporal Risk Levels for Intelligent Vehicles: A Data-driven Approach
Zheyu Zhang, Boyang Wang, Chao Lu +3
In recent years, road safety has attracted significant attention from researchers and practitioners in the intelligent transport systems domain. As one of the most common and vulne…
Sequential Point Cloud Prediction in Interactive Scenarios: A Survey
Haowen Wang, Zirui Li, Jianwei Gong
Point cloud has been widely used in the field of autonomous driving since it can provide a more comprehensive three-dimensional representation of the environment than 2D images. Po…
Life-Long Multi-Task Learning of Adaptive Path Tracking Policy for Autonomous Vehicle
Cheng Gong, Jianwei Gong, Chao Lu +2
This paper proposes a life-long adaptive path tracking policy learning method for autonomous vehicles that can self-evolve and self-adapt with multi-task knowledge. Firstly, the pr…
Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot
Cheng Gong, Zirui Li, Xingyu Zhou +3
Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility. Yet their potential has not been fully…
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks
Lianzhen Wei, Zirui Li, Jianwei Gong +2
Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of…
Regeneration and Joining of the Learned Motion Primitives for Automated Vehicle Motion Planning Applications
Boyang Wang, Jianwei Gong, Wenli Liang +1
How to integrate human factors into the motion planning system is of great significance for improving the acceptance of intelligent vehicles. Decomposing motion into primitives and…
Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications
Boyang Wang, Jianwei Gong, Ruizeng Zhang +1
Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algo…
VPH+ and MPC Combined Collision Avoidance for Unmanned Ground Vehicle in Unknown Environment
Kai Liu, Jianwei Gong, Huiyan Chen
There are many situations for which an unmanned ground vehicle has to work with only partial observability of the environment. Therefore, a feasible nonholonomic obstacle avoidance…