✔ Created safety critical perception system from the ground up.
✔ Built proofs of concept and conducted advanced research on connected and automated vehicles.
✔ Developed perception and localization modules for L4 autonomous driving applications.
✔ Collaborated with system/component engineers and supplier partners to support the product design during the full development cycle.
✔ Provided design directions to ensure the timely launch of the product.
✔ Contributed to the successful completion of 5 consulting projects in a collaborative team environment.
✔ Provided industrial solutions for various fields, including biomedical engineering (human organ numerical model), multi-physics (wind turbine simulation), and structural optimization (weight reduction project)
Thesis: Phase field modeling of the defect evolution and failure
Advisor: Professor Marisol Koslowski
Thesis: Numerical Modeling of Coalbed Methane Well Testing with IMPES method.
Advisor: Dr. Junfeng Zhang
Object detection/tracking/fusion based on Apollo in ROS. Complete lidar/camera/radar perception pipeline.View Project
Automated Valet Parking demo presented by Magna at CES 2019. Worked on low cost localization solution.View Demo
Achieved the 1st team to successfully implement a completely autonomous vehicle system on a Lincoln MKZ, including 3 integrated modules of perception, planning, and control, based on ROS and Autoware. Go Vulture!View Project
Predicted 3D bounding boxes of vehicles and pedestrians from Lidar point cloud and camera images and exploited multimodal sensor data and automatic region-based feature fusion to maximize the accuracy.View Demo
Implemented a real-time processing system for simultaneously localizing the vehicle and building high- precision maps over large areas with 3D details from Lidar point cloud data.View Demo
Built a path planning algorithm using Finte State Machine in C++ for a car to navigate a 3-lane highway efficiently, and generated smooth and safe path using localization, sensor fusion and map data.View Project
Detected lane-lines in the camera image using OpenCV by employing camera calibration, color transformation, gradient operation and identifying the lane-line pixels. Predicted the curvature of the road as well as the off-center distance of the vehicle.View Project
Recognized vehicles in the camera image by performing a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and trained a linear Support Vector Machine (SVM) classifier in OpenCV.View Project
Developed a Convolution Neural Network (CNN) in Keras that can predict steering angles from road images, and created video of good human driving behavior in simulator to train the model. After training, the model can drive the car autonomously around the track successfully.View Project
Built a Deep Neural Network (DNN) using TensorFlow and trained the model using the public German Traffic Sign Dataset with GPU acceleration. The model is able to reach accuracy of 91.2% on test set after training.View Project
Implemented Extended and Unscented Kalman Filter in C++ to execute the sensor fusion of noisy Lidar and Radar measurements and estimate the moving state with lower than 0.1 m error.View Project
Designed a 2D Particle Filter in C++ to locate the vehicle, given a map, chatter sensor and control data.View Project
Controlled the vehicle to drive around the track using Model Predictive Control (MPC) in C++, and resolved the latency issue of actuators, such as steering angle and throttle/brake pedal.View Project
I am a road enthusiast. Check out the roads I’ve driven on so far here.