Curriculum Vitae

Xiao Wang is a research assistant and PhD student in the Cyber-Physical Systems Group under Prof. Dr.-Ing. Matthias Althoff since 2019. She graduated with the Master of Science degree in Mechanical Engineering from the Technische Universität München, Germany, in 2018. She received the Bachelor of Engineering degree in Vehicle Engineering from Tongji University, Shanghai, China. 

Her research focuses on motion planning for autonomous vehicles, formal methods, and safe reinforcement learning.

Offered Theses:

Open theses:

If you are interested in this topic or in her research and want to write a thesis in this area, please feel free to contact her. General ideas could be to apply formal method to standard RL techniques to increase safety, to benchmark state-of-art safe RL algorithms, to learn driving behaviors from real traffic data, or to increase sample efficiency of RL for motion planning, etc.

Supervised theses:

• [BT | SS19]: Christoph Pillmayer - "Online Verification for Autonomous Vehicles using Motion Primitives and Deep Reinforcement Learning" 
• [BT | SS19]: Hagen Winkelmann - "Learning Cost Functions for Sampling Based Planners in Autonomous Driving"
• [MT | WS19]: Hanna Krasowski - "Safe Reinforcement Learning for Autonomous Vehicles"
• [MT | SS20]: Xi Chen - "Learning Driving Policies Using Reinforcement Learning Combined with Sampling-based Motion Planner"
• [MT | SS20]: Kailiang Dong - "Generative Adversarial Imitation Learning for Highway Autonomous Driving with Safety Guarantees"
• [MT | WS20]: Zhenyu Li - "Safe Reinforcement Learning for Continuous Control Tasks"
• [MT | SS21]: Jiaying Huang - "An Online Verification Framework for Autonomous Driving Using Sampling-Based Motion Planners"
• [MT | WS21]: Guyue Huang - "Safety Falsification for Black-Box Motion Planners of Autonomous Vehicles"
• [MT | WS21]: Daniel Tar - "Ensuring Safety for Reinforcement-Learning-Based Motion Planners Using Online Verification"
• [MT | WS21]: Jinyue Guan - "Ensuring Drivability of Fail-safe Trajectories for Autonomous Vehicles using Reinforcement Learning Methods"
• [MT | WS21]: Christoph Pillmayer - "Constrained Reinforcement Learning for Autonomous Driving"
• [MT | SS22]: Murat Can Üste - "Generation of Naturalistic Traffic Rule Violations Using Imitation Learning"

Ongoing theses:

• BT: Philip Haitzer - "Benchmarking Multi-agent Reinforcement Learning for Cooperative Driving"

Teaching

WS 2020/21

• Exercise: Techniques in Artificial Intelligence
• Practical Course: Motion Planning for Autonomous Vehicles

SS 22

• Seminar: Cyber-Physical Systems
• Practical Course: Motion Planning for Autonomous Vehicles

WS 2019/20

• Exercise: Techniques in Artificial Intelligence

SS 19

• Seminar: Cyber-Physical Systems
• Master Practical Course: Motion Planning for Autonomous Vehicles

WS 2018/19

• Exercise: Techniques in Artificial Intelligence
• Practical Course: Motion Planning for Autonomous Vehicles