Keshu Wu
Research Topics </>
Interaction Relational Reasoning for Motion Generation and Planning

Interaction Relational Reasoning for Motion Generation and Planning
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I have explore the dynamics of relational reasoning and interaction modeling, particularly for trajectory prediction and planning. This involved constructing sophisticated models capable of learning dynamic interaction graphs and hypergraphs based on behaviors of agents, with a particular emphasis on predicting future relational dynamics. I have integrated this knowledge into interaction-aware decision-making processes, combining it with multi-modal motion prediction and the modeling of physical dynamics to enhance motion planning and predictive capabilities.

Interaction-aware Multi-modal Vehicle Motion Prediction

Interaction-aware Multi-modal Vehicle Motion Prediction

I developed a dynamic graph approach to accurately represent bidirectional and heterogeneous interactions within vehicle motion states. I also proposed a future-guided graph embedding approach to link historical and future motion states, highlighting the correlation of future motions between surrounding vehicles and the target vehicle. Additionally, I provided predictions for both longitudinal and lateral driving behaviors in a multi-modal manner, assigning probabilities to potential future trajectories.

Transportation Network Missing Data Reconstruction

Transportation Network Missing Data Reconstruction

I proposed a generic, cost-effective informatization framework within an intelligent transportation system, significantly reducing reliance on fixed sensors by using mobile sensors for improved data collection and accessibility. I developed a generic sensor network that utilized sparsely located sensors, including fixed sensors, drones, and floating vehicles, to gather data with characterized spatial and temporal sparsity. Furthermore, I specialized in inferring and reconstructing unobserved information by training models with partially observed data, enabling the generalization of traffic data reconstruction in real-world scenarios with missing values.

C-V2X Connected Corridor Data Pipeline

C-V2X Connected Corridor Data Pipeline

I developed a data pipeline system using socket programming to connect with Roadside Unit (RSU) devices at intersections, enabling the acquisition of real-time traffic signal data. I processed this data in accordance with SAE J2735 standards and managed its storage using a PostgreSQL database. Additionally, I created an interactive dashboard to display both historical and real-time traffic signal data from RSUs. My work also involved conducting tests with On-Board Unit (OBU) devices in connected vehicles to receive and process information from RSU devices at intersections. Furthermore, I implemented a digital twin to simulate the collected data in a high-fidelity virtual environment, facilitating real-time monitoring, comprehensive analysis, and behavior simulation.

Autonomous Driving Simulation and Modeling

Autonomous Driving Simulation and Modeling

I conducted extensive research on collaborative automated driving systems (CADS) using the CARLA simulation environment. This involved simulating the operational life cycle of CADS (sensing - prediction - planning - control), and validating the algorithms developed for collaborative driving. My focus for the sensing function was on developing and integrating roadside camera-based object detection and tracking systems. For prediction, I embedded and verified the effectiveness of motion prediction algorithms within the environment. In terms of planning and control, I designed and explored vehicle control strategies and deep reinforcement learning models, executing path planning as well as longitudinal and lateral vehicle control.

© Keshu Wu (08/2024)