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Industrial Speaker to the Automation and Control Engineering Program of Politecnico di Milano

Today I got a great pleasure to be an invited industrial speaker to the students of the Automation and Control Engineering Program of Politecnico di Milano, one of the largest programs I know so far in the field (i.e. with more than 400 control engineering students). I was excited with the opportunity to share and inspire the students with Siemens Simcenter, Digital Twin and engineering technologies, in particular our team developing autonomous vehicle control and testing solutions. Thank you again Prof. Lorenzo Fagiano for the invitation!

[Paper] Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving

We present the development and deployment of an embedded optimal control strategy for autonomous driving applications on a Ford Focus road vehicle.Non-linear model predictive control (NMPC) is designed and deployed on a system with hard real-time constraints. We show the properties of sequential quadratic programming (SQP) optimization solvers that are suitable for driving tasks

The Role of Vehicle Dynamics in Autonomous Driving?

We show a demo comparison for an autonomous lane change scenario, where the same vehicle control system was applied to two vehicle dynamics models

New Master Thesis Students from TU Delft

Covid time and new rules make it harder for both companies and universities. Still, this semester our R&D team at Siemens Digital Industries Software just welcomed three Master thesis/intern students coming from TU Delft Master of Robotics: Kevin Voogd, Jianfeng Cui, and Yurui Du.

[Paper]  IEEE IV 2021, IEEE ITSC 2021, JSAE 2021

In the last few months, we have made some more progress to tackle some autonomous driving challenges in industry.

[Demo] Testing on Real Vehicle

Testing and validation are big challenges in the autonomous vehicles driving industry. Your ADAS engineers keep developing new perception or control algorithms, but what is an efficient process starting from there to vehicle deployment?

Model predictive control (MPC) has become popular in autonomous control.

The key is to optimize control actions over a prediction horizon, which is often short due to computation.

IEEE Control System Society Associate Editor

This is my third year serving in the control system society as Associate Editor for conferences like CDC and ACC. A small role but I find this editorial work is an interesting way to keep updated with (and learn from) latest developments regularly from academics, and at the same time provide my R&D industry visions to the community!

Great to hear that. Looking back I have supervised some students doing thesis/internship in the company in the last 2-3 years, and six of them found their profesional careers after study at different teams of Siemens.

Push Control to the Limit

People actually wants more from autonomous car, be cautious but also aggressive when necessary, be able and flexible moving along the tradeoff curves until physical limitation.

[Demo] From Development (Perception, Planning, Control) to Validation Framework

Besides, a nice example of collaboration not only within the team engineers but also with Marketing dept., who captures well the main messages, on-site ADAS demos and put them in such illustrative video

[Demo] Designing ADAS algorithms to Enhance Safety and Comfort

Next challenge is having an efficient framework from simulation to physical testing to validate your designs. This demo shows an example of our team works on MPC control development.