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20202026
most citedAutonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks

10 citations · 26 across the 13 of their papers we have counts for

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10 papers · 1 filter

cs.RO2026

Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving

Cheng Gong, Haoyang Wang, Chao Lu +2

Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations. However, most learning-based…

cs.RO2024★ 6 cited

Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation

Yuchun Wang, Cheng Gong, Jianwei Gong +1

Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires conside…

cs.RO2024★ 6 cited

Beyond Imitation: A Life-long Policy Learning Framework for Path Tracking Control of Autonomous Driving

C. Gong, C. Lu, Z. Li +3

Model-free learning-based control methods have recently shown significant advantages over traditional control methods in avoiding complex vehicle characteristic estimation and para…

cs.RO2022

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…

cs.RO2022

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…

cs.RO2021★ 2 cited

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…

cs.RO2021

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…

cs.RO2021★ 2 cited

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…

cs.RO2021

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…

cs.RO2021★ 10 cited

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…