Tudor Nicosevici is the architect for perception and fusion for Premium Solutions in PL Cruise, Autonomous Mobility, being involved in technical coordination of artificial intelligence, environment perception and understanding activities for autonomous driving. He has been with Continental Automotive for the last 7 years, in technical and management positions, and was awarded several technical and business excellence awards.
Previously, he worked with Computer Vision and Robotics Group, University of Girona and Coronis Computing, coordinating projects for applied research and technology transfer between the academia and the industry sector. His areas of expertise are computer vision, robotics and artificial intelligence, with a M.Sc. in sensor fusion and a PhD in robotics and computer vision.
The Pop in Your Job – What drives you? Why do you love your topic?
Our society has progressed significantly in the last decades, in more ways than one. This progress comes with an increased need for agile and efficient mobility. However, this sector has yet to make significant progress to meet our expectations: it is still responsible for an important percentage of our carbon footprint, we are still exposed to traffic accidents on daily basis, and we still waste a significant amount of time during our daily commutes. Towards solving these challenges, the automotive sector is currently witnessing a paradigm shift,
moving away from classic algorithms towards data-driven and machine-learning systems. While these systems show a lot of promise in terms of performance, we need to continuously focus on building robust safety mechanisms into them. These are exciting times for me personally. As a human, I am excited to have the opportunity to contribute to the progress of a technology sector with such an impact in our society. As an engineer, I am driven by the challenges and complexity of intelligent mobility. Particularly, I am interested in contributing to the adoption of innovative technologies in a safe way, into the mobility industry.
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