Future is Automation

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

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

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


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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

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.

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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 

Conference version: 24th IEEE ITSC (September 2021) 

Poster: TRB 2022

Journal: Journal of Intelligent and Connected Vehicles

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

1. Implement the Convex Feasible Set algorithm for real-time path planning 

2. Compare Convex Feasible Set with SQP, iterative SQP for performance evaluation