This page shows a portfolio of projects that have been done or doing since my graduate study at The Ohio State University. They mainly consist of pedestrian-related problems or issues in the autonomous driving. The topics ranges from perception, prediction, motion planning and control of autonomous driving tasks that require interaction with pedestrians.
Below is just a list of projects, detailed description is coming soon.
Online Pedestrian/Object Detection
- Image-based 2D object/pedestrian detection by deep neural network (ResNet-34).
- Pointcloud-based 3D object detection by rule-based segementation.
- Calibration of a combined camera and LiDAR sensor system.
Interactive Pedestrian Motion Modeling for Traffic Scenario Simulation
- Social force based modeling of pedestrian/crowd motion considering vehicle effect.
Pedestrian Trajectory Prediction
- Neural network (LSTM) based interactive pedestrian trajectory prediction.
- Reachable set analyis of pedestrian behavior.
- Created two pedestrian trajectory datasets that covers multi-pedestrian interaction with vehicles:
- Standardized different pedestrian trajectory dataset for better benchmark configuration.
Vehicle Motion Planning in Mixed Environment
- Local trajectory planning (lattice planning) for obstacle bypassing based on optimizing high-order polynomials in the Frenet frame.
- Path planning in the unstructured environment by A*, modified RRT, and spline fitting.
Longitudinal Speed Control of Vehicle Interacting Crossing Pedestrians
- Longitudinal speed regulation by model predictive control (MPC) with the prediction of moving obstacles by reachability set analysis.