Edmond Irani Liu joined the Cyber-Physical Systems Group in 2019 as a Research Assistant and Ph.D. student under the supervison of Prof. Dr.-Ing. Matthias Althoff. In 2015 and 2018, he received his bachelor's degree in Automation and master's degree in Control Science and Engineering, both from Shanghai Jiao Tong University, respectively.

His current research focuses on traffic rule-compliant motion planning and cooperative driving with safety guarantee. He is a participant in the project Cooperative and Intrinsically-Correct Control of Vehicles in Diverse Environments (CoInCIDE) within the Priority Program Cooperatively Interacting Automobiles (SPP1835), funded by the German Research Foundation (DFG); He is also a participant in the Huawei-TUM collaboration project Research on Key Technologies of Safety Assurance for Autonomous Vehicles.

I am always looking for self-motivated students to solve interesting problems arising in my research areas. If you are interested in one of the currently available topics, simply send me a mail (with title "Bachelor / Master Thesis Application - Your Name") with your up-to-date CV and transcript of records attached.

Currently Available:

• -

Ongoing:

• [BA | 2022] Specification-compliant maneuver planning via reachable sets
• [MA | 2022] Cooperative Motion Planning of Automated Vehicles using Reachable Sets
• [BA | 2022] Comparing Invariably Safe Sets with Responsibility-Sensitive Safety
• [BA| 2022] Cooperative Decision Making of Automated Vehicles using Monte Carlo Tree Search.

Finished:

• [MA | 2021] Motion Planning for Autonomous Vehicles Using Rapidly Exploring Random Trees and Reachable Sets
• [MA | 2021] Maneuver Planning of Automated Vehicles Considering Traffic Rules
• [BA | 2021] Group Formation of Automated Vehicles with Set-based Prediction
• [MA | 2020] Computation of Reachable Sets for Multi-UAV Motion Planning Applications
• [BA | 2019] Globetrotter - Automatic Extraction of Interesting Road Networks Around the World
• [BA | 2019] Generation of Interactive Benchmark for Motion Planning of Autonomous Vehicles

• Practical course - Motion Planning for Autonomous Vehicles (since WS2019)
  • Developing a Toolbox for Computing the Reachable Sets of Automated Vehicels
  • Developing a Toolbox for Computing the Invariably Safe Sets of Automated Vehicles
  • Driving Virtually from Garching to IAA
  • Formation of Cooperative Groups for Automated Vehicles via Set-based Prediction
  • Improving a Graph Search-Based Motion Planning Algorithm
  • CommonRoad Interactive Benchmark Generation
  • Developing a Hierarchical A* Motion Planning Algorithm
• Seminar course - Cyber-Physical Systems (since WS2019)
  • Cooperative Driving of Automated Vehicles
  • Motion Planning with Temporal Logic Specifications
  • Safe and Efficient Cooperation Strategies at Intersections
• Lecture - Foundations of Artificial Intelligence (since WS2019)
  • Programming exercise: CommonRoad Search: Search-based Motion Planners with Motion Primitives

A collection of useful material for research can be found here.

Last updated: 11.10.2021