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Posts by Collection
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publications
DBO Trajectory Planning and HAHP Decision-Making for Autonomous Vehicle Driving on Urban Environment
Published in IEEE Access 2019, 2019
We propose a Driving Behaviour-Oriented (DBO) trajectory planner and Hierarchical AHP (HAHP) decision-maker for intelligent vehicles. Unlike purely minimizing distance/time, our approach ensures actuator constraints, comfort, and strict traffic rule compliance for structured road driving.
Learning how to avoiding obstacles for end-to-end driving with conditional imitation learning
Published in International Conference on Signal Processing and Machine Learning (SPML 2019), 2019
We use CARLA, an autonomous driving simulator, to collect 6 hours of human driver reactions to obstacles under given commands (follow, go straight, turn left, turn right). We propose a Behavior-Cloning network with a modified loss function that emphasizes steering errors for higher accuracy. Results show that image augmentation is crucial for training, and a speed limit helps prevent unexpected stops.
Real-time Multiple Path Prediction and Planning for Autonomous Driving aided by FCN
Published in 6th CAA International Conference on Vehicular Control and Intelligence (CVCI) 2022, 2022
We propose FCN-A, a real-time multiple path planning method combining semantic segmentation with the traditional graph-based search. A fully convolutional neural network (FCN) was first designed to learn the optimal path area generated by an A based path planning method in various real and simulated environments. By injecting noises into localization information, the generalization ability of the neural network is greatly enhanced facing inaccurate localization results. Then, multiple possible path areas inferred by the FCN are adopted as constraints for the following A* based path planning.
Multi-agent decision-making at unsignalized intersections with reinforcement learning from demonstrations
Published in IEEE Intelligent Vehicles Symposium (IV) 2023, 2023
We propose QMIXwD to pre-train the policy using demonstration data consisting of expert data and interaction data to improve the initial performance of agents and improve exploration, as well as to reduce the distributional shift between the demonstration data and the environmental interaction data.
Safe reinforcement learning with dead-ends avoidance and recovery
Published in IEEE Robotics and Automation Letters (RA-L) 2023, 2023
We propose a method to construct a boundary that discriminates between safe and unsafe states. The boundary we construct is equivalent to distinguishing dead-end states, indicating the maximum extent to which safe exploration is guaranteed, and thus has a minimum limitation on exploration.
How to fine-tune the model: unified model shift and model bias policy optimization
Published in Annual Conference on Neural Information Processing Systems (NeurIPS) 2023, 2023
We theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process, adaptively adjusting model updates to get a performance improvement guarantee while avoiding model overfitting.
Batch Informed Vines (BIV*): Heuristically Guided Exploration of Narrow Passages by Batch Vine Expansion
Published in IEEE Robotics and Automation Letters (RA-L) 2024, 2024
We propose an enhanced heuristic-based vine expansion method, termed Batch Informed Vines (BIV). BIV utilizes path information from the current search tree as heuristics to prioritize the exploration of narrow passages leading to lower solution cost. Additionally, we propose a batch vine expansion strategy, which includes exploration of “Closer to Unexplored Obstacle” (CTUO) nodes and batch expansion.
Focus on what matters: Separated models for visual-based rl generalization
Published in Annual Conference on Neural Information Processing Systems (NeurIPS) 2024, 2024
We propose SMG, which utilizes a reconstruction-based auxiliary task to extract task-relevant representations from visual observations and further strengths the generalization ability of RL agents with the help of two consistency losses.
CERTAIN: Context Uncertainty-aware One-Shot Adaptation for Context-based Offline Meta Reinforcement Learning
Published in International Conference on Machine Learning (ICML) 2025, 2025
We propose CERTAIN to tackle context ambiguity and OOD issues in one-shot adaptation for COMRL by leveraging uncertainty-aware task representation learning and context collection. Build upon heteroscedastic-like uncertainty estimation, our method can identify unreliable contexts and then lead to more robust policies.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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