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Transfusor: Transformer Diffusor for Controllable Human-like Vehicle Trajectory Generation
Transfusor: Transformer Diffusor for Controllable Human-like Vehicle Trajectory Generation
Goal: Generate human-like lane-changing trajectories following certain condition
Paper coming soon
1. Develop a generative model with only a limited number of training samples
2. Achieve robust and realistic lane-changing trajectories with different aggressiveness level
3. Greatly enrich the virtual simulation test scenarios for AV testing
LiDAR-Based Cooperative Relative Localization
LiDAR-Based Cooperative Relative Localization
Conference: IV Symposium (June 2023, coming soon)
Goal: Reduce relative localization with LiDAR points registration
1. Develop a robust localization framework for multi-CAV cooperative perception
2. Achieve fast and efficient relative localization and reduce 80 % GNSS errors
3. Save communication bandwidth by only transforming a small portion of points
Online Media 23.movModel-based RL framework for collision avoidance
Model-based RL framework for collision avoidance
Poster: TRB 2023
1. Optimal control + Reinforcement learning to avoid the collision when others are "at fault"
2. Trajectory predicting model (DL based)
3. Dynamic trajectory planning and optimization
Spatio-weighted information fusion and DRL-based control for connected autonomous vehicles
Spatio-weighted information fusion and DRL-based control for connected autonomous vehicles
Conference version: 23 rd IEEE ITSC (September 2020). Poster: TRB 2021
Journal: Transportation Research Part C: Emerging Technology May 2021
Goal: faster, safer, comfort lane-changing decisions
1. Data fusion framework for both local information (from sensors) and global information (from connectivity devices) ----> dynamic shape input
2. Q learning as a lane-changing decision processor
3. Optimal connectivity range analysis.
Rayne - OBS-animation2 流畅版.mp4A DRL-based Multiagent Cooperative Control Framework for CAV Networks: a Graphic Convolution Q Network
A DRL-based Multiagent Cooperative Control Framework for CAV Networks: a Graphic Convolution Q Network
Journal: Computer‐aided Civil and Infrastructure Engineering (April 2021):
Poster: TRB 2021; Full paper: Arxiv
Goal: Cooperative lane changes to merge out the ramps
1. GCN-based information fusion block to enforce communication and cooperation between vehicles
2. Combine GCN and DQN into an end-to-end multi-agent decision processor to control the CAVs lane changing decisions for a road segment.
Image transformer for explainable autonomous driving system
Image transformer for explainable autonomous driving system
Conference version: 24th IEEE ITSC (September 2021)
Poster: TRB 2022
Journal: Journal of Intelligent and Connected Vehicles
Image transformer-based end-to-end computer vision model.
Generating driving decisions and explanations.
Achieve SOTA with significantly superior performance and lower computational cost compared to the benchmark model.
CAV and Multi-Agent Reinforcement Learning for Mitigating Highway Bottleneck Congestion
CAV and Multi-Agent Reinforcement Learning for Mitigating Highway Bottleneck Congestion
Abridged version: Arxiv
Journal version: Transportation Metrica Part A.
Goal: Achieve variable speed limit to reduce bottleneck congestion using CAVs
1. Reinforcement learning + Graphic neural network
2. Bottleneck congestion mitigation with variable speed limit (VSL)
Convex optimization based path planning for autonomous vehicles
Convex optimization based path planning for autonomous vehicles
1. Implement the Convex Feasible Set algorithm for real-time path planning
2. Compare Convex Feasible Set with SQP, iterative SQP for performance evaluation
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