Hello! My name is Yu Zhang(张玉). I am a PhD candidate working under the supervision of Prof. Huiyan Chen in School of Mechanical Engineering at Beijing Institute of Technology. I was also a visiting student working under the supervision of Prof. Steven L. Waslander at WAVELab in University of Waterloo from 2015 to 2017.
My research interests cover the area of motion planning and control algorithms, simulation for autonomous cars. My focus is more about optimization-based and sampling-based motion planning algorithms subject to differential contraints. I developed several handy simulation tools to support motion planning and control algorithms testing of autonomous driving system based on V-REP as well.
I was a main contributor to several awesome autonomous driving projects from motion planning and simulation aspects: Autonomoose, Skyline(Renesas Car), BlackBerry QNX Autonomous Prototype Vehicle from 2016 to 2017.
In 2017, I attended the CES(Consumer Electronics Show) in Las Vegas to show the functional safety, autonomy of our autonomous driving system running on low power consumption R-Car H3 SoC. It’s a big success!
- Real-time Motion Planning
- Optimization-based and Search-based Motion Planning with Differential Constraints
- Obstacle Avoidance and Collision Checking
- Simulation for Developments and Evaluations of Motion Planning Algorithms
- Machine Learning Techniques that Improve the Performance and Efficiency of Classical Motion Planning Methods
Our paper “Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization” is accepted by the Sensors(Switzerland) journal.
Our paper “Speed Planning for Autonomous Driving via Convex Optimization” is accepted for presentation at the 21st IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2018) which will be held in Maui, Hawaii, USA in November 2018.
Our paper “Hybrid Trajectory Planning for Autonomous Driving in Highly Constrained Environments” is accepted by the IEEE Access journal and the preprint version is avaliable at https://ieeexplore.ieee.org/document/8375948/
Our preprint “Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization” paper is now available at: http://www.preprints.org/manuscript/201805.0164/v2