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Data-driven control has become more popular recently, where measurement data is exploited to design higher performance controller, robust to uncertainties and change of environment.

Getting more insights of human driving preferences (i.e. when and how to brake, steering at different traffic scenarios), perceived safety, different cultures and styles,... are essential to improve ADAS control development

Last week, I had the pleasure of visiting Oxford Control Group and deliver a seminar there. It was an excellent opportunity to meet and discuss with great researchers in control systems, data-driven, and safety-based optimization fields. I was also delighted to hear that our works have inspired some PhDs there on their research :) .

4-5 years ago we developed an intuition to deal with this challenge, learning from control barrier function (CBF) from legged robotics community.

Autonomous vehicle dataset such as nuScenes, Waymo Motion, or your own collected one, is known valuable for algorithm development and testing. What's more?

The goal of this project is to enhance safety and comfort in autonomous driving. Altogether we are developing and advancing different ADAS technologies, i.e. data collection, processing, scenario extraction-generation, algorithms (AI, control), XiL testing (X = model, hardware, human), and validation using several autonomous vehicle platforms.

While keep pushing the boundary of AI technologies in safety-critical applications like autonomous vehicles and medical applications, at Siemens we have been jointly develop with FOCETA partners technologies like requirements formulation, critical scenario generation, safety monitoring, explainable AI, adversarial examples....

Bring real collected data into simulation (Real2Sim) is essential activity in ADAS testing, allow engineers to test different sensor, vehicle dynamics models, as well as various what-if traffic scenarios.

This semester we have a pleasure to welcome two Master thesis students working full time with our ADAS team in Siemens Digital Industries, Leuven office: Jasper van Leuven (TUDelft, Netherlands) and Sven Becker (EPFL, Switzerland).

This picture shows a Digital Twin vehicle together with the real Red Bull F1 vehicle (that Siemens partnering with), that has been inspiring us on developing Digital Twin for ADAS, autonomous driving.

ADAS or autonomous driving dataset is captured from vehicle sensors, and often used for perception algorithms or motion prediction. However, logged data is often passive, static, and open-loop. It is not able to actively adapt or evolve, for example with respect to the new sensor types. As the data is with human driver providing vehicle actions, closed-loop control testing is not possible.