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Today marks my 7-year PhD graduation from KU Leuven as well as the time working at Siemens Digital Industries Software. My experiences and ambitions of innovation keep rolling :).

Following the exciting Formula Student event last month, FSG and Siemens is organizing a workshop where we will show some tools, applications and algorithms around Digital Twin that can help you to develop autonomous driving functions more efficient. Check the below infor for more information.

See following demo from Siemens research engineer Jean Pierre Allamaa, done within the EU ELO-X Marie Curie project and in collaboration with Prof. Toshiyuki Ohtsuka (Kyoto Univ.) during a secondment program.

SADAS performance is often evaluated based on safety, and from the outsider's perspective, i.e. check if there is a collision. What is not commonly known is that perceived safety and comfort are similarly important in ADAS development. See below a short interesting demo from insider or passenger's view on lane change maneuvers. "

1. Reinforcement Learning from Simulation to Real World; 2. MPC-Based Imitation Learning for Human-Like Autonomous Driving; 3. Critical Driving Behaviours Using Driver's Risk Field

Data-driven control: advanced optimal control, imitation learning, driver behavior models, reinforcement learning

A great pleasure to act as a judge for the interesting Formula Student Germany event this week, in the driverless category. Seeing many talented, motivated students working and speaking on similar technologies to what we are doing in ADAS industry ;).

It was exciting to welcome the MECO group from KU Leuven last Friday at Siemens Digital Industries Software in Leuven.

This period we have had a pleasure to welcome two visiting PhDs: Shuhao Zhang and Renzi Wang from KULeuven, who are visiting our ADAS team at Siemens Digital Industries Software office in Leuven for 3-4 months. Both is working on ADAS challenges related to learning perception and motion planning under uncertainty conditions, with driving data from Siemens.

Glad to see quite some Digital Twin research activities from UK robotics and control peers in the workshop.

Learning from human driving demonstration is an interesting approach to improve autonomous driving policy. Still, it has some challenges: 1. majority logged data is from normal traffic situations, not sufficient critical and diverse scenarios data; and 2. during training and validation, the other traffic actors do not react to the ego's car policy, or no actual closed-loop response.

It is a nice way to start the weekend after a busy week: Our ADAS R&D team just received notifications that 3 papers got accepted to IFAC World Congress - (probably the biggest event of the Control system society this year). A nice achievement of the team members.