A Freeway Traffic Flow Prediction Model Based on a Generalized Dynamic Spatio-Temporal Graph Convolutional Network

IEEE Transactions on Intelligent Transportation Systems, 2024

Gan, R., An, B., Li, L., Qu, X., & Ran, B.

Proposes a dynamic spatio-temporal GCN for freeway traffic flow prediction.

[Paper]

Vehicular Speed Prediction Method for Highway Scenarios Based on Spatio-Temporal Graph Convolutional Networks and Potential Field Theory

IEEE Internet of Things Journal, 2024

Li, L., An, B., Zhang, D., Gan, R., Zhou, Z., Qu, X., & Ran, B.

Integrates STGCN and potential field theory for vehicular speed prediction on highways.

[Paper]

Distributed Computing for Autonomous Vehicles

US Patent App. 18/762,995, 2024

Ran, B., Fu, S., Gan, R., Cheng, Y., Li, S., Tian, K., Chen, T., Dong, S., Shi, K., Shi, H., & others.

Patent describing distributed computing frameworks for autonomous vehicles.

[Paper]

Goal-based Neural Physics Vehicle Trajectory Prediction Model

AED50, Transportation Research Board 104th Annual Meeting (Washington, D.C., Jan 2025), 2025

Gan, R., Shi, H., Li, P., Wu, K., An, B., Li, L., Ma, J., Ma, C., & Ran, B.

Presents a goal-based neural physics approach for vehicle trajectory prediction.

[Paper] [Slides]

V2X-LLM: Improving Vehicle-to-Everything Integration and Understanding with Large Language Models

ACP30, Transportation Research Board 104th Annual Meeting (Washington, D.C., Jan 2025), 2024

Wu, K., **Gan, R.**, You, J., Cheng, Y., Li, P., Zhu, J., & Parker, S. T.

Efficient Large-Scale Traffic Forecasting via Multi-Subgraph Spatio-Temporal Graph Convolutional Networks

ACP30, Transportation Research Board 104th Annual Meeting (Washington, D.C., Jan 2025), 2024

An, B., Cui, C., Gan, R., Li, L., Qu, X., & Ran, B.

V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

ACP30, Transportation Research Board 104th Annual Meeting (Washington, D.C., Jan 2025), 2024

You, J., Shi, H., Jiang, Z., Huang, Z., Gan, R., Wu, K., Cheng, X., Li, X., & Ran, B.

Generalized Spatio-Temporal Graph Convolution Networks with Dynamic Information for Traffic Speed Prediction

ACP40, Transportation Research Board 104th Annual Meeting (Washington, D.C., Jan 2023), 2023

Gan, R., An, B., Li, L., Qu, X., & Ran, B.

Real-World Data Inspired Interactive Connected Traffic Scenario Generation

ACP30, Transportation Research Board 104th Annual Meeting (Washington, D.C., Jan 2025), 2023

You, J., Li, P., Cheng, Y., Wu, K., Gan, R., Parker, S. T., & Ran, B.

DEGCN: A Novel Dynamic Edge Graph Convolution Network for Microscopic Behavior Prediction of Traffic Flow

AED50, Transportation Research Board 104th Annual Meeting (Washington, D.C., Jan 2023), 2022

An, B., Li, L., Gan, R., Qu, X., & Ran, B.

How Does C-V2X Perform in Urban Environments? Results from Real-world Empirical Experiments on Urban Arterials

(TRBAM-24-04495) AED20 Applications and Innovations in Urban Travel Data, Transportation Research Board 103th Annual Meeting (Washington, D.C., Jan 2024), 2022

Li, P., Wu, K., Cheng, Y., Huang, Z.,Gan, R., Parker, S., & Noyce, D. A.

A DDPG-Based Variable Speed Limit Control for Consecutive Bottleneck

AED30, Transportation Research Board 104th Annual Meeting (Washington, D.C., Jan 2022), 2021

Gan, R., Qu, X., Mao, P., Li, L., & Ran, B.

Goal-based Neural Physics Vehicle Trajectory Prediction Model

arXiv preprint arXiv:2409.15182, 2024

Gan, R., Shi, H., Li, P., Wu, K., An, B., Li, L., Ma, J., Ma, C., & Ran, B.

A trajectory prediction model using neural physics with goal-based constraints.

[Paper]

FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction

arXiv preprint arXiv:2411.16747 (submitted to CVPR), 2024

You, J., Gan, R., Tang, W., Huang, Z., Liu, J., Jiang, Z., Shi, H., Wu, K., Long, K., Fu, S., & others.

A noise conditional diffusion approach for car-following trajectory prediction.

[Paper]

V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

arXiv preprint arXiv:2408.09251, 2024

You, J., Shi, H., Jiang, Z., Huang, Z., Gan, R., Wu, K., Cheng, X., Li, X., & Ran, B.

Introduces a vision-language model approach to V2X cooperative autonomous driving.

[Paper]

Real-World Data Inspired Interactive Connected Traffic Scenario Generation

arXiv preprint arXiv:2409.17429, 2024

You, J., Li, P., Cheng, Y., Wu, K., Gan, R., Parker, S. T., & Ran, B.

Generates connected traffic scenarios using real-world data for interactive simulations.

[Paper]

Bridging Physical and Language Models: A Physics-Constrained Large Language Model Framework for Human-Centric Driving Safety

, 2024

Gan, R., Li, P., An, B., Ma, J., & Ran, B.

Proposes a physics-constrained LLM framework targeting safety in human-centric driving.

Ethical Decision-Making in Autonomous Vehicles: A Reinforcement Learning Approach for Fair Risk Management

, 2023

Gan, R., Li, L., Nan, T., Ma, J., & Ran, B.

Applies reinforcement learning to ethical AV decision-making for balanced risk.